Automatic facility accident reporting augmented by worker event tracking and correlation

ABSTRACT

Tracked locations and time-logging information of smart walkie-talkies enable improved event detection and worker profiling in a facility. Interactions between a worker and facility equipment, as well as collaborations between the worker and other workers are determined based on mapping the tracked locations of a smart walkie-talkie used by the worker. A machine learning model is trained and implemented to use information related to these interactions and collaborations to build experience profiles and evidence profiles for the worker. For example, the machine learning model is trained to generate a particular format for an experience profile that emphasizes significant experience or work by the worker by way of the determined interactions and collaborations.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application claims priority to and the benefits of U.S. ProvisionalApplication No. 63/347,490 entitled “APPARATUSES AND COMMUNICATIONNETWORKS FOR DEVICE TRACKING AND GEOFENCING” filed on May 31, 2022, andU.S. Provisional Application No. 63/371,293 entitled “APPARATUSES ANDCOMMUNICATION NETWORKS FOR DEVICE TRACKING AND GEOFENCING” filed on Aug.12, 2022. The entire disclosures of the aforementioned applications areherein incorporated by reference as part of the disclosure of thisapplication.

TECHNICAL FIELD

The present disclosure is generally related to wireless communicationhandsets and systems.

BACKGROUND

Traditional methods to monitor facilities are used to performinspections in particular environments. Some methods use Radio-FrequencyIdentification (RFID) badges to monitor frontline workers using a readerat a gate or other entryway. The badges can be used to allow entry forauthorized persons to gain access. Frontline workers are typicallydisallowed from carrying smartphones, tablets, or portable computers onsite. When there is an emergency, a worker may need to alert others.However, traditional methods and systems for communication within, andmonitoring of, manufacturing and construction facilities sometimes haveinadequate risk management and safeguards, lack an efficient structure,or can suffer from unrealistic risk management expectations or poorproduction forecasting.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a block diagram illustrating an example architecture for anapparatus implementing device tracking using geofencing, in accordancewith one or more embodiments.

FIG. 2A is a drawing illustrating an example environment for apparatusesand communication networks for device tracking and geofencing, inaccordance with one or more embodiments.

FIG. 2B is a flow diagram illustrating an example process for generatinga work experience profile using apparatuses and communication networksfor device tracking and geofencing, in accordance with one or moreembodiments.

FIG. 3 is a drawing illustrating an example facility using apparatusesand communication networks for device tracking and geofencing, inaccordance with one or more embodiments.

FIG. 4 is a drawing illustrating example apparatuses for device trackingand geofencing, in accordance with one or more embodiments.

FIG. 5 is a drawing illustrating example apparatuses for device trackingand geofencing, in accordance with one or more embodiments.

FIG. 6 is a drawing illustrating example charging cradles forapparatuses implementing device tracking and geofencing, in accordancewith one or more embodiments.

FIG. 7 is a drawing illustrating example charging cradles forapparatuses implementing device tracking and geofencing, in accordancewith one or more embodiments.

FIG. 8 is a drawing illustrating example charging cradles forapparatuses implementing device tracking and geofencing, in accordancewith one or more embodiments.

FIG. 9 is a drawing illustrating example charging cradles forapparatuses implementing device tracking and geofencing, in accordancewith one or more embodiments.

FIG. 10 is a drawing illustrating example drainage holes for chargingcradles for apparatuses implementing device tracking and geofencing, inaccordance with one or more embodiments.

FIG. 11 is a diagram illustrating geofencing and geofenced-basedcommunication within a facility or worksite, in accordance with one ormore embodiments.

FIG. 12 is a flow diagram illustrating an example process forresponse-controlled communications for geofenced areas, in accordancewith one or more embodiments.

FIG. 13 is a diagram illustrating an example system for visualizationand storage of temporally-dynamic smart radio location, in accordancewith one or more embodiments.

FIG. 14 is a flow diagram illustrating an example process forclassifying worker activity based on smart radio locations withrole-specific activity areas, in accordance with one or moreembodiments.

FIG. 15 is a drawing illustrating an example user interface forvisualizing worker activity data, in accordance with one or moreembodiments.

FIGS. 16A and 16B are drawings illustrating example techniques forgeofencing a specific area, in accordance with one or more embodiments.

FIG. 17 is an illustration of “blind” operation of a smart radio.

FIG. 18 is a cross-sectional diagram of a smart radio illustratingspeaker placement.

FIG. 19 is a flowchart illustrating automatic roaming of channels.

FIGS. 20A and 20B illustrate a message thread user interfaceimplementing long presses as a push-to-talk feature.

FIG. 21 is a flowchart illustrating power mode selection.

FIG. 22 is a block diagram illustrating an example machine learning (ML)system, in accordance with one or more embodiments.

FIG. 23 is a block diagram illustrating an example computer system, inaccordance with one or more embodiments.

DETAILED DESCRIPTION

The embodiments disclosed herein describe methods, apparatuses, andsystems for device tracking and geofencing. Construction, manufacturing,repair, utility, resource extraction and generation, and healthcareindustries, among others, rely on real-time monitoring and tracking offrontline workers, individuals, inventory, and assets such asinfrastructure and equipment. In some embodiments, a portable and/orwearable apparatus, such as a smart radio, a smart camera, or a smartenvironmental sensor that records information, downloads information,communicates with other apparatuses or a cellphone tower, and detectsgas levels, or temperature is used by frontline workers to providecompliance, quality, or safety. Some embodiments of the presentdisclosure provide lightweight and low-power apparatuses that are wornor carried by a worker and used to monitor information in the field, ortrack the worker for logistical purposes. The disclosed apparatusesprovide alerts, locate resources for workers, and provide workers withaccess to communication networks. The wearable apparatuses disclosedenable worker compliance and provide assistance with operator tasks.

The advantages and benefits of the methods, systems, and apparatusesdisclosed herein include solutions for confined-space management usinglive video feeds, gas detection, and analysis of entry and exit timesfor personnel using smart devices. The disclosed systems enable theprovision of video collaboration software for the industrial field usingstreamlined enterprise-grade video with interactive meetingcapabilities. Workers join from the field on their apparatuses withoutrelying on software integrations or the purchase of additional software.Some embodiments disclosed enable workers to view other workers'credentials and roles such that participants know the level of expertisepresent. The systems further enable the location of workers who arecurrently out in the field using a facility map that is populated byinformation from smart radios, smart cameras, or smart sensors.

Among other benefits and advantages, the disclosed systems providegreater visibility compared to traditional methods within a confinedspace of a facility for greater workforce optimization. The digital timelogs for entering and exiting a facility measure productivity levels onan individual basis and provide insights into how the weather at outdoorfacilities in different geographical locations affects workers. The timetracking technology enables visualization of the conditions a frontlineworker is working under while keeping the workforce productive andprotected. In addition, the advantages of the machine learning (ML)modules in the disclosed systems include the use of shared weights inconvolutional layers, which means that the same filter (weights bank) isused for each node in a layer. The weight structure both reduces memoryfootprint and improves performance for the system.

The smart radio embodiments disclosed that include Radio over InternetProtocol (RoIP) provide the ability to use an existing Land Mobile Radio(LMR) or Digital Mobile Radio (DMR) system for communication betweenworkers, allowing a company to bridge the gap that occurs through theprocess of digitally transforming their systems. Communication is thusmore open because legacy systems and modern apparatuses communicate withfewer barriers, the communication range is not limited by the radioinfrastructure because the smart radios use the Internet, and costs arereduced for a company to provide communication apparatuses to theirworkforce by obviating more-expensive, legacy radios. The smartapparatuses enable workers to provide field observations to reportsafety issues in real-time to mitigate risk, prevent hazards, and reducetime barriers to drive operational performance. Workers in the field usethe smart apparatuses to more-quickly notify management of potentialsafety issues or issues that are causing delays. The apparatuses enablemass notifications to rapidly relay information to a specific subgroup,provide real-time updates for evacuation, and transmit accurate locationpins.

The smart apparatuses disclosed reduce the need for workers to wearmultiple, cumbersome, non-integrated, and potentially distractivedevices into one user-friendly, comfortable, and cost-effective smartdevice. Advantages of the smart radio disclosed include ease of use forcarrying in the field during extended durations due to its smaller size,relatively low power consumption, and integrated power source. The smartradio is sized to be small and lightweight enough to be regularly wornby a worker. The modular design of the smart radio disclosed enablesquick repair, refurbishment, or replacement. The apparatuses are sharedbetween workers on different shifts to control inventory as needed. Thesmart apparatuses only work inside a facility geofence, reducing theimpulse to steal.

The present disclosure also refers to smart radios and smart apparatusesas smart walkie-talkies, two-way radio transceivers or two-way radiotransceiver devices, communication handsets, shared communicationdevices, industrial radio devices, and/or the like. As will beunderstood by one of skill in the field of the present disclosure, asmart radio (or other disclosed terms) generally refers to acommunication device for transmitting and receiving communicationsignals (e.g., radio signals) with other communication devices, such asother smart radios. More particularly, the present disclosure refers toa class of communication devices (including the smart radios,walkie-talkies, two-way transceiver devices, radio handsets as referredto throughout) that is understood as distinct from conventional consumercommunication devices, such as smartphones (e.g., iPhones), tablets,media players, and the like. This class of communication devices, asrepresented by a walkie-talkie for example, is configured to providecommunication capabilities in fields that require reliability,ruggedness, efficiency and/or low overhead in direct communication, andsecurity. For example, walkie-talkies or two-way radio transceiverdevices include specific features that are well-suited for use in anindustrial workplace (e.g., a chemical plant, a manufacturing facility),in military field operations, in network-sparse locations, or inamateur/amusement activities. An example of such a feature present inwalkie-talkies or two-way radio transceiver devices is a push-to-talk(PTT) key that improves the ability of a user to quickly communicate toanother device via coarse user control (e.g., through gloved hands).Another example feature is a walkie-talkie having local on-devicecontrol or selection of its own operating channel (e.g., a radiofrequency channel or band) that itself uses to communicate with otherdevices, thus offering flexibility without adherence to network-dictatedchannel schemes/allocations. While these features can realize therequirements associated with the aforementioned fields or settings,these same features can be absent in conventional consumer communicationdevices, which may also be inappropriate for those fields or settingsdue to fragility, due to distracting/irrelevant/extraneous userfeatures, due to reliance on cellular network infrastructure, due tocost, and the like. Indeed, a smartphone device can lack such features,due to a lack of a need for a smartphone user to control (withspecificity) the parameters of communication with other users. Throughapplied ingenuity and effort, unique and intelligent enhancements forthis distinct class of communication devices have been developed (andare described herein) to improve the functionality and operation ofthese communication devices, while preserving their distinctapplicability to certain fields or settings.

Embodiments of the present disclosure will be described more thoroughlyfrom now on with reference to the accompanying drawings. Like numeralsrepresent like elements throughout the several figures, and in whichexample embodiments are shown. However, embodiments of the examples areembodied in many different forms and should not be construed as limitedto the embodiments set forth herein. The examples set forth herein arenon-limiting examples and are merely examples, among other possibleexamples. Throughout this specification, plural instances (e.g., “224”)implement components, operations, or structures (e.g., “224 a”)described as a single instance. Further, plural instances (e.g., “224”)refer collectively to a set of components, operations, or structures(e.g., “224 a”) described as a single instance. The description of asingle component (e.g., “224 a”) applies equally to a like-numberedcomponent (e.g., “224 b”) unless indicated otherwise. These and otheraspects, features, and implementations are expressed as methods,apparatuses, systems, components, program products, means, or steps forperforming a function, and in other ways. These and other aspects,features, and implementations will become apparent from the followingsections, including the examples. Any of the embodiments described ineach section can be used with one another and features of eachembodiment are not necessarily exclusive to the described embodimentsuch that the headings are not limiting.

Smart Radio

FIG. 1 is a block diagram illustrating an example architecture for anapparatus 100 implementing device tracking using geofencing, inaccordance with one or more embodiments. The apparatus 100 isimplemented using components of the example computer system 2300illustrated and described in more detail with reference to FIG. 23 . Inembodiments, the apparatus 100 is used to execute the machine learning(ML) system 2200 illustrated and described in more detail with referenceto FIG. 22 . The architecture shown by FIG. 1 is incorporated into aportable wireless apparatus 100, such as a smart radio, a smart camera,a smart watch, a smart headset, or a smart sensor. FIGS. 4-5 showdifferent views of an exemplary smart radio that includes thearchitecture of the apparatus 100 shown in FIG. 1 . Likewise, differentembodiments of the apparatus 100 include different and/or additionalcomponents and are connected in different ways.

The apparatus 100 shown in FIG. 1 includes a controller 110communicatively coupled electronically either directly or indirectly toa variety of wireless communication arrangements, a position estimatingcomponent 123 (e.g., a dead-reckoning system), which estimates currentposition using inertia, speed, and intermittent known positions receivedfrom a position tracking component 125, which in embodiments, is aGlobal Navigation Satellite System (GNSS) component, a display screen130, an optional audio device 140, a user-input device 150, and a dualbuilt-in camera 165 (another camera, 160, is on the other side of thedevice). A battery 120 is electrically coupled with a private Long-TermEvolution (LTE) wireless communication subsystem 105, a Wi-Fi subsystem106, a low-power wide area network (LPWAN), for example, long range(LoRa) protocol subsystem 107, Bluetooth subsystem 108, barometer 111,audio device 140, user-input device 150, and built-in camera 160 forproviding electrical power. Battery 120 is electrically andcommunicatively coupled with controller 110 for providing electricalpower to controller 110 and enabling controller 110 to determine astatus of battery 120 (e.g., a state-of-charge). In embodiments, battery120 is a removable rechargeable battery.

Controller 110 is, for example, a computer having a memory 114,including a non-transitory storage medium for storing software 115, anda processor 112 for executing instructions of the software 115. In someembodiments, controller 110 is a microcontroller, a microprocessor, anintegrated circuit (IC), or a system-on-a-chip (SoC). Controller 110includes at least one clock capable of providing time stamps anddisplaying time via display screen 130. The at least one clock isupdatable (e.g., via the user interface 150, a global positioning system(GPS) navigational device, the position tracking component 125, theInternet, a private cellular network subsystem, the server 170, or acombination thereof).

In embodiments, the apparatus 100 (e.g., implemented as a smart radio asshown by FIG. 4 ) communicates with a worker ID badge and a chargingstation using near field communication (NFC) technology. NFC devicesgenerally act as electronic identity devices. Examples of an NFC rubberpad 804 and an NFC tag 808 located on a charging cradle are depicted inmore detail in FIG. 8 . An NFC-enabled device, such as a smart radio,also operates like an NFC tag or card, allowing a worker to performtransactions such as clocking in for the day at a worksite or facility,making payments, clocking out for the day, or logging in to a computersystem of the facility. An example facility 300 is illustrated anddescribed in more detail with reference to FIG. 3 . The smart radiocommunicates with the charging station using NFC in one or bothdirections.

Workers entering a facility carry or wear an identification (ID) badgethat has an NFC tag (and optionally an RFID tag) embedded in the badge.The NFC tag in the worker's ID badge stores personal information of theworker. Examples include name, employee or contractor serial number,login credentials, emergency contact(s), address, shifts, roles (e.g.,crane operator), any other professional or personal information, or acombination thereof. When the worker arrives for a shift, they pick asmart radio up off the charging station and tap their ID badge to thesmart radio. The NFC tag in the ID badge communicates with an NFC modulein the smart radio to log the worker into the smart radio andlogin/clock the worker in to the workday. In embodiments, the worker'spersonal information is stored in the cloud computing system 220.

In some embodiments, when a smart radio is picked up off a chargingstation by a worker arriving at the facility, the smart radio operatesas a time clock to record the start time for the worker at the facility.In some embodiments, the worker logs in to the facility system using atouchscreen or buttons of the smart radio. Example buttons of a smartradio are illustrated and described in more detail with reference toFIG. 5 . For example, the smart radio records the time the worker signedin with their badge or other credentials and assigns the start time andlocation to the identity of the worker in the system. The smart radiosends the start time and the location to a local server at the facilityor to the cloud computing system 220. The cloud computing system 220uses the start time and stop time (when the worker places the smartradio back on the charging station) to maintain a record of the hoursthe worker worked (e.g., to determine the pay owed the worker).

In some embodiments, removing the smart radio from the charging stationbegins a timer that upon expiration, causes the smart radio to trigger asiren noise. The timer is halted or removed by the worker logging in tothe smart radio (e.g., via tapping their ID card to the smart radio). Ifthe smart radio is not provisioned when removed from the chargingstation, the system had a reduced ability to track the smart radio andthe user thereof. By initiating a siren, the smart radio draws attentionto the failure to log in.

The cloud computing system 220 stores, manages, and updates shifts,contacts, and roles for each worker, project, and facility. A shiftrefers to a planned set period of time during which the worker(optionally with a group of other workers) performs their duties. Theworkday is divided into shifts. A worker is assigned one or more shifts(e.g., 9:00 a.m.-5:00 p.m. on Monday and Wednesday) to work and theassignments are stored, managed, and updated by the cloud computingsystem 220 based in part on time logging information received from thesmart radios and other smart apparatuses (as shown by FIG. 2A). Theworker has one or more roles (e.g., lathe operator, lift supervisor) forthe same or different shifts. For each role and shift, the worker hasone or more contacts (e.g., emergency contact(s), supervisorycontact(s), etc.) assigned to the worker. The contacts are stored,managed, and updated by the cloud computing system 220 based in part ontime logging information received from the smart radios. For example,the information reflects that the 9:00 a.m.-5:00 p.m. Monday shift hasconcluded, and the contacts are updated for the next shift of theworker.

In an example, a worker, Alice, begins their shift using a particularsmart radio. After Alice picks up the smart radio and clocks in, Aliceis introduced to Bob, her emergency contact. Alice can further accessthe name and contact information for the emergency contact, Bob,assigned to Alice for that shift using the smart radio. Three hourslater, Bob's shift ends and Bob clocks out. A next shift (Chuck's shift)begins, however, Alice is still working on their shift. Chuck is Alice'snew emergency contact. Alice is not necessarily aware of the change.However, the smart radio that Alice is using will automatically reflectthat the emergency contact is now Chuck. The cloud computing system 220thus stores, manages, and updates shifts, contacts, and roles for eachworker, project, and facility. The information is updated based in parton time logging information received from the smart radios and othersmart apparatuses (as shown by FIG. 2A). The cloud computing system 220updates each smart radio with the information (on roles and contacts)needed for a shift when a worker clocks in using the radio.

In some embodiments, roles are assigned on a tiered basis. For example,Alice has roles assigned to her as an individual, as connected to thecontract she is working, and as connected to her employer. Each of thosetiers operates identity management within the cloud computing system220. Each user frequently will work with others they have never metbefore and do not have the contact information thereto. Frontlineworkers tend to collaborate across employers or contracts. Based ontiered assigned roles, the relevant contact information for workers on agiven task/job is shared therebetween. “Contact information” asfacilitated by the smart radio is governed by the user account in eachsmart radio (e.g., as opposed to a phone number connected to a cellularphone).

In another example, Alice begins their shift using a particular smartradio. After Alice picks up the smart radio and clocks in, Alice canaccess the name and contact information for the emergency contact, Bob,assigned to Alice for that shift using the smart radio. Three hourslater, when the shift ends and Alice clocks out, a next shift (Chuck'sshift) begins. Chuck picks up the same (or a different) smart radio toclock in for their shift. If Chuck is using the same smart radio thatAlice just used, the smart radio will automatically reflect that theemergency contact is now the emergency contact (Darla) assigned to Chuckfor the next shift. After Chuck picks up the smart radio and clocks in,Chuck can access the name and contact information for the emergencycontact, Darla, assigned to Chuck for the next shift using the smartradio. If Chuck is using a different smart radio from the radio thatAlice used, the different smart radio will also automatically reflectthat the emergency contact is now the emergency contact (Darla) assignedto Chuck for the next shift. The cloud computing system 220 thus stores,manages, and updates shifts, contacts, and roles for each worker,project, and facility. The information is updated based in part on timelogging information received from the smart radios and other smartapparatuses (as shown by FIG. 2A). The cloud computing system 220updates each smart radio with the information (on roles and contacts)needed for a shift when a worker clocks in using the radio. While thisexample describes emergency contacts being updated for a smart radio, itis understood that forms of contact information/data or preferredcontact configurations can be dynamically updated for a smart radiobased on current users. That is, other than emergency contacts,preferred contact configurations provided to smart radios according to alog-in of a current user can include frequent contacts, supervisorcontacts, team member contacts, and/or the like. These preferred contactconfigurations can be maintained by the cloud computing system 220 foreach user.

The information transmitted from the cloud computing system 220 to eachsmart radio to specifically update and configure each smart radio caninclude user identities for enabling access to various networks via thesmart radio, including facility-specific private networks and commercialcellular networks. For example, after Alice logs in to the smart radio,the smart radio receives user identity information associated with Alice(e.g., from the cloud computing system 220) that enables the smart radioto connect to private networks for which Alice is authorized.

In embodiments, a front-facing camera of the smart radio is used tocapture employee clock-ins to deter “buddy clocking” or “buddypunching,” whereby one worker fraudulently records the time of another.For example, the smart radio or cloud computing system 220 operates afacial recognition system (e.g., using the ML system 2200 illustratedand described in more detail with reference to FIG. 22 ), eliminatingthe need of a fingerprint scanner. Cloud-based software running on thesmart radio enables the time logging mechanism to work seamlessly withthe cloud computing system 220. In embodiments, Human Resources (HR)software is used for tracking employee time, and can, in versions,interact with smart radios or other devices to track and record when aworker enters a particular facility, or portion of a facility, and atwhat time each entry occurs. In order to gain access to a particularprotected area of a facility, a worker uses NFC functionality of thesmart radio to scan an NFC device located at an entry point, is allowedaccess, and the HR application records the time access was granted. Thesmart radios can also be used to scan NFC tags or cards mounted atlocations (e.g., vessels and equipment). In embodiments, the machinelearning system 2200, illustrated and described in more detail withreference to FIG. 22 , is used to detect and track abnormalities in timelogging, for example, using features based on the number of workersclocking in or facility slowdowns as input data.

In embodiments, the smart radio and the cloud computing system 220 havegeofencing capabilities. The smart radio allows the worker to clock inand out only when they are within a particular Internet geolocation. Ageofence refers to a virtual perimeter for a real-world geographic area,(e.g., a portion of a facility). For example, a geofence is dynamicallygenerated for the facility (as in a radius around a point location) ormatched to a predefined set of boundaries (such as construction zones orrefinery boundaries, or around specific equipment). A location-awaredevice (e.g., the position tracking component 125 and the positionestimating component 123) of the smart radio entering or exiting ageofence triggers an alert to the smart radio, as well as messaging to asupervisor's device (e.g., the text messaging display 240 illustrated inFIG. 2A), the cloud computing system 220 or a local server. Theinformation, including a location and time is sent to the cloudcomputing system 220. In embodiments, the machine learning system 2200,illustrated and described in more detail with reference to FIG. 21 , isused to trigger alerts, for example, using features based on equipmentmalfunctions or operational hazards as input data.

The wireless communications arrangement includes a cellular subsystem105, a Wi-Fi subsystem 106, the optional LPWAN/LoRa network subsystem107 wirelessly connected to a LPWAN network 109, and a Bluetoothsubsystem 108, all enabling sending and receiving. Cellular subsystem105, in embodiments, enables the apparatus 100 to communicate with atleast one wireless antenna 174 located at a facility (e.g., amanufacturing facility, a refinery, or a construction site). Forexample, the wireless antennas 174 are permanently installed ortemporarily deployed at the facility. Example wireless antennas 374 areillustrated and described in more detail with reference to FIG. 3 .

In embodiments, a cellular edge router arrangement 172 is provided forimplementing a common wireless source. A cellular edge routerarrangement 172 (sometimes referred to as an “edge kit”) is usable toinclude a wireless cellular network into the Internet. In embodiments,the LPWAN network 109, the wireless cellular network, or a local radionetwork is implemented as a local network for the facility usable byinstances of the apparatus 100, for example, the local network 204illustrated and described in more detail with reference to FIG. 2A. Forexample, the cellular type can be 2G, 3G, 4G, LTE, 5G, etc. The edge kit172 is typically located near a facility's primary Internet source 176(e.g., a fiber backhaul or other similar device). Alternatively, a localnetwork of the facility is configured to connect to the Internet usingsignals from a satellite source, transceiver, or router 178, especiallyin a remotely located facility not having a backhaul source, or where amobile arrangement not requiring a wired connection is desired. Morespecifically, the satellite source plus edge kit 172 is, in embodiments,configured into a vehicle, or portable system. In embodiments, thecellular subsystem 105 is incorporated into a local or distributedcellular network operating on any of the existing 88 different EvolvedUniversal Mobile Telecommunications System Terrestrial Radio Access(EUTRA) operating bands (ranging from 700 MHz up to 2.7 GHz). Forexample, the apparatus 100 can operate using a duplex mode implementedusing time division duplexing (TDD) or frequency division duplexing(FDD).

A Wi-Fi subsystem 106 enables the apparatus 100 to communicate with anaccess point capable of transmitting and receiving data wirelessly in arelatively high-frequency band. In embodiments, the Wi-Fi subsystem 106is also used in testing the apparatus 100 prior to deployment. ABluetooth subsystem 108 enables the apparatus 100 to communicate with avariety of peripheral devices, including a biometric interface device116 and a gas/chemical detection device 118 used to detect noxiousgases. In embodiments, the biometric and gas-detection devices 116 and118 are alternatively integrated into the apparatus 100. In embodiments,numerous other Bluetooth devices are incorporated into the apparatus100.

As used herein, the wireless subsystems of the apparatus 100 include anywireless technologies used by the apparatus 100 to communicatewirelessly (e.g., via radio waves) with other apparatuses in a facility(e.g., multiple sensors, a remote interface, etc.), and optionally withthe cloud/Internet for accessing websites, databases, etc. The wirelesssubsystems 105, 106, and 108 are each configured to transmit/receivedata in an appropriate format, for example, in IEEE 802.11, 802.15,802.16 Wi-Fi standards, Bluetooth standard, WinnForum Spectrum AccessSystem (SAS) test specification (WINNF-TS-0065), and across a desiredrange. In embodiments, multiple apparatuses 100 are connected to providedata connectivity and data sharing across the multiple apparatuses 100.In embodiments, the shared connectivity is used to establish a meshnetwork.

The position tracking component 125 and the position estimatingcomponent 123 operate in concert. In embodiments, the position trackingcomponent 125 is a GNSS (e.g., GPS) navigational device that receivesinformation from satellites and determines a geographical position basedon the received information. The position tracking component 125 is usedto track the location of the apparatus 100. In embodiments, a geographicposition is determined at regular intervals (e.g., every five seconds)and the position in between readings is estimated using the positionestimating component 123.

GPS position data is stored in memory 114 and uploaded to server 170 atregular intervals (e.g., every minute). In embodiments, the intervalsfor recording and uploading GPS data are configurable. For example, ifthe apparatus 100 is stationary for a predetermined duration, theintervals are ignored or extended, and new location information is notstored or uploaded. If no connectivity exists for wirelesslycommunicating with server 170, location data is stored in memory 114until connectivity is restored, at which time the data is uploaded, thendeleted from memory 114. In embodiments, GPS data is used to determinelatitude, longitude, altitude, speed, heading, and Greenwich mean time(GMT), for example, based on instructions of software 115 or based onexternal software (e.g., in connection with server 170). In embodiments,position information is used to monitor worker efficiency, overtime,compliance, and safety, as well as to verify time records and adherenceto company policies.

In some embodiments, a Bluetooth tracking arrangement using beacons isused for position tracking and estimation. For example, Bluetoothcomponent 108 receives signals from Bluetooth Low Energy (BLE) beacons.The BLE beacons are located about the facility similar to the examplewireless antennas 374 shown by FIG. 3 . The controller 110 is programmedto execute relational distancing software using beacon signals (e.g.,tri angulating between beacon di stance information) to determine theposition of the apparatus 100. Regardless of the process, the Bluetoothcomponent 108 detects the beacon signals and the controller 110determines the distances used in estimating the location of theapparatus 100.

In alternative embodiments, the apparatus 100 uses Ultra-Wideband (UWB)technology with spaced apart beacons for position tracking andestimation. The beacons are small battery powered sensors that arespaced apart in the facility, and broadcast signals received by a UWBcomponent included in the apparatus 100. A worker's position ismonitored throughout the facility over time when the worker is carryingor wearing the apparatus 100. As described herein, location sensing GNSSand estimating systems (e.g., the position tracking component 125 andthe position estimating component 123) can be used to primarilydetermine a horizontal location. In embodiments, the barometer componentis used to determine a height that the apparatus 100 is located at (oroperate in concert with the GNSS to determine the height) using knownvertical barometric pressures at the facility. With the addition of asensed height, a full three-dimensional location is determined by theprocessor 112. Applications of the embodiments include determining if aworker is, for example, on stairs or a ladder, atop or elevated inside avessel, or in other relevant locations.

An external power source 180 is optionally provided for rechargingbattery 120. The battery 120, in embodiments, is shaped, sized, andelectrically configured to be receivable into a charging station (notshown by FIG. 1 ). Example charging stations (also referred to as“charging cradles” or “charging docks”) are illustrated and described inmore detail with reference to FIG. 6 . An example charging cradle 800 isshown by FIG. 8 . An example two-dimensional array of charging cradles900 is shown by FIG. 9 . In embodiments, the architecture of theapparatus 100 shown by FIG. 1 includes a connector that connects to theexternal power source 180.

In embodiments, display screen 130 is a touch screen implemented using aliquid-crystal display (LCD), an e-ink display, an organiclight-emitting diode (OLED), or other digital display capable ofdisplaying text and images. An example text messaging display 240 isillustrated in FIG. 2A. In embodiments, display screen 130 uses alow-power display technology, such as an e-ink display, for reducedpower consumption. Images displayed using display screen 130 include butare not limited to photographs, video, text, icons, symbols, flowcharts, instructions, cues, and warnings. For example, display screen130 displays (e.g., by default) an identification style photograph of anemployee who is carrying the apparatus 100 such that the apparatus 100replaces a traditional badge worn by the employee. In another example,step-by-step instructions for aiding a worker while performing a taskare displayed via display screen 130. In embodiments, display screen 130locks after a predetermined duration of inactivity by a worker toprevent accidental activation via user-input device 150.

The audio device 146 optionally includes at least one microphone (notshown) and a speaker for receiving and transmitting audible sounds,respectively. Although only one speaker is shown existing in thearchitecture drawing of FIG. 1 , it should be understood that in anactual physical embodiment, multiple speakers (and also microphones usedfor the purpose of noise cancellation) are utilized such that theapparatus 100 can adequately receive and transmit audio. In embodiments,the speaker has an output around 105 dB to be loud enough to be heard bya worker in a noisy facility. The speaker adjusts to ambient noise, forexample, the audio device 146 or a circuit driving the speaker samplesthe ambient noise, and then increases a volume of the output audio fromthe speaker such that the volume is greater than the ambient noise(e.g., 5 dB louder). In embodiments, a worker speaks commands to theapparatus 100. The microphone of the audio device 146 receives thespoken sounds and transmits signals representative of the sounds tocontroller 110 for processing. In embodiments, the machine learningsystem 2200, illustrated and described in more detail with reference toFIG. 22 , is used to generate appropriate volume levels, for example,using features based on noise at a location or manufacturing operationtypes as input data.

In embodiments, the audio device 146 disseminates audible information tothe worker via the speaker and receives spoken sounds via themicrophone(s). The audible information is generated by the apparatus 100based on data or signals received by the apparatus 100 (e.g., the smartcamera 228 illustrated and described in more detail with reference toFIG. 2A) from the cloud computing system 220, an administrator, or alocal server. For example, the audible information includesinstructions, reminders, cues, and/or warnings to the worker and is inthe form of speech, bells, dings, whistles, music, or otherattention-grabbing noises without departing from the scope hereof. Inembodiments, one or more speakers of the apparatus 100 (e.g., the smartradio illustrated in FIG. 4 ) are adapted to emit sounds from a frontside 404, a back side 408, any of the other sides 412, 416 of the smartradio, or even multiple sides of the smart radio.

In embodiments, the apparatus 100 is continuously powered on. Forexample, an option to turn off the apparatus 100 is not available to aworker (e.g., an operator without administrator privileges). If thebattery 120 discharges below a cut-off voltage, such that the apparatus100 loses power and turns off, the apparatus 100 will automatically turnon upon recharging of battery 120 to above the cut-off voltage. Inoperation, the apparatus 100 enters a standby mode when not actively inuse to conserve battery charge. Standby mode is determined viacontroller 110 to provide a low-power mode in which no data transmissionoccurs and display screen 130 is in an OFF state. In the standby mode,the apparatus 100 is powered on and ready to transmit and receive data.During use, the apparatus 100 operates in an operational mode. Inembodiments, the display screen 130, upon activation, is configured todisplay a battery level (e.g., a state-of-charge) indication. Theindicator is made to be presented due to processes running on controller110 (e.g., which detect voltage from a voltmeter electrically coupledwith battery 180 and electronically connected with the controller 110).

Communication Network Features

FIG. 2A is a drawing illustrating an example environment 200 forapparatuses and communication networks for device tracking andgeofencing, in accordance with one or more embodiments. The environment200 includes a cloud computing system 220, cellular towers 212, 216, andlocal networks 204, 208. Components of the environment 200 areimplemented using components of the example computer system 2300illustrated and described in more detail with reference to FIG. 23 .Likewise, different embodiments of the apparatus 100 include differentand/or additional components and are connected in different ways.

Smart radios 224, 232 and smart cameras 228, 236 are implemented inaccordance with the architecture shown by FIG. 1 . In embodiments, smartsensors implemented in accordance with the architecture shown by FIG. 1are also connected to the local networks 204, 208 and mounted on asurface of a worksite, or worn or carried by workers. For example, thelocal network 204 is located at a first facility and the local network208 is at a second facility. An example facility 300 is illustrated anddescribed in more detail with reference to FIG. 3 . In embodiments, eachsmart radio and other smart apparatus has two (Subscriber IdentityModule) SIM cards, sometimes referred to as dual SIM. A SIM card is anIC intended to securely store an international mobile subscriberidentity (IMSI) number and its related key, which are used to identifyand authenticate subscribers on mobile telephony devices. Inembodiments, the two SIM cards includes a first SIM for connecting to afacility-specific network, such as a private network, and a second SIMfor connecting to a commercial network that is not facility-specific. Inembodiments, the SIMS of a smart radio are dynamically configured withuser identities based on a current or present user of the smart radio.For example, Alice logs in to the smart radio, and the smart radioconfigures at least one of its SIM according to a user identityassociated with Alice, such that the smart radio can access privatenetworks for which Alice is authenticated or authorized.

A first SIM card enables the smart radio 224 a to connect to the local(e.g., cellular) network 204 and a second SIM card enables the smartradio 224 a to connect to a commercial cellular tower (e.g., cellulartower 212) for access to mobile telephony, the Internet, and the cloudcomputing system 220 (e.g., to major participating networks such asVerizon™, AT&T™, T-Mobile™). The local network 204 can be specific tothe facility; for example, only certain users designated for thefacility can access and communicate over the local network 204. In someembodiments, the users that can access the local network 204 or aprivate facility-specific network are selected by the cloud computingsystem 220. In such embodiments, the smart radio 224 a has two radiotransceivers, one for each SIM card. In other embodiments, the smartradio 224 a has two active SIM cards, and the SIM cards both use onlyone radio transceiver. However, the two SIM cards are both active onlyas long as both are not in simultaneous use. As long as the SIM cardsare both in standby mode, a voice call (e.g., a Radio over InternetProtocol (RoIP) call, a Voice over Internet Protocol (VoIP) call, atelephone call, a cellular network call) could be initiated on either.However, once the call begins, the other SIM becomes inactive until thefirst SIM card is no longer actively used.

According to example embodiments, the use of multiple SIM cards or SIMcomponents by the smart radio is controlled by a location of the smartradio relative to geofences defined for the facility. In someembodiments, the facility is associated with facility-specific locationdata (e.g., stored, configured, managed by the cloud computing system)that defines one or more geofences for facility-specific activities. Forexample, the geofences correspond to buildings or structures within thefacility, staging areas, production areas, hazardous zones, and/or thelike. Generally, the geofences correspond to areas in facility in whichlocal, private, facility-specific, and/or employer/contract-specificcommunication is needed, desired, or useful. In some embodiments, thesmart radio obtains the facility-specific location data, for example,when a user logs-in on the smart radio.

Whenever the smart radio is located within a geofence defined by thefacility-specific location data and designated for facility-specificactivities and communication, the smart radio uses a facility-specificSIM component to connect to and/or communicate over a local privatenetwork for the facility. Conversely, whenever the smart radio exits thegeofence (e.g., into a non-geofenced area, into a geofence designatedfor non-facility-specific or public activities, into a geofence definedfor threshold service quality by a commercial cellular network), thesmart radio responsively uses a non-facility-specific SIM component toconnect to and/or communicate over a non-facility-specific network, suchas a commercial cellular network. In particular, the smart radioswitches from the local private network to the commercial cellularnetwork, for example, and disconnects from the local private network.

In some embodiments, the smart radio prevents connection to the localprivate network when located outside of the defined geofences, forexample, based on disabling the facility-specific SIM component. Thegeofence SIM selection scheme prevents the smart radio from connectingto a network with spotty or questionable service. For example, a networkthat is supported inside a building is less effective outside of thebuilding, but the smart radio may still receive communication from theassociated access point. Rather than allow the smart radio to remainoperating on the low quality network associated with the inside of thebuilding, the smart radio, now in a new outside geofence, connects to adifferent network using the non-facility specific SIM. Thus, thegeofences implement an artificial failure state for causing a smartradio to transition from using a private network to a commercialcellular network. Monitoring of location relative to geofences thusprecludes a need to evaluate respective signal qualities of privatenetworks and public networks to determine which network to use.

In some embodiments, the geofences are used to automatically configureor program the two-way radio, DMR, or LMR rather than determine anactive SIM card. For example, a given facility site has a license to usea particular wireless band, and a transmit power allowance. The licensefor the site enables the use of encrypted elements of the radiotransmissions as well so that the licensed band remains private. Thesmart radio thus provisions itself to the licensed band and transmitpower (and encryption if applicable) when entering an associatedgeofence with that facility site. Similarly, when the smart radio entersa different facility site with a difference geofence, the smart radioprovisions for the wireless band and transmit power that is available atthat site. While the smart radio is not within a corresponding geofencethe smart radio is provisioned for public radio bands and transmitpower. In some embodiments, the provisioning is inclusive rather thanexclusive, that is the radio band and power available includes bothpublic bands and transmit powers as well as the local private band.

Provisioning the smart radio to make use of the site specific band makesuse of a wireless specification stored on board the smart radio.Multiple wireless specifications may exist on the smart radio asconnected to locations, work orders, or people. The specifications areused to provision the wireless transceiver used for two-way radiocommunication. Transmit power and band filtering are settings of thetransceiver that may be provisioned on an ad hoc basis.

In addition to the geofence location, the smart radio's logincredentials are another element that embodiments apply to band, transmitand encryption settings of the radio features. That is, even if a userhas a smart radio and is within a given geofence, that geofence may onlyapply to particular login credentials or users of a particular status(e.g., managers or associated with a given work order).

In embodiments, the local network 204 uses a private address space of IPaddresses. The local network 204 can provide resources, such as variousinterfaces, data windows, and other resources described herein (e.g.,generated or provided by the cloud computing system 220) related to theoperation of the facility via the private address space. In otherembodiments, the local network 204 is a local radio-based network usingpeer to peer two-way radio (duplex communication) with extended rangebased on hops (e.g., from smart radio 224 a to smart radio 224 b tosmart radio 224 c). Hence, radio communication is transferred similar toaddressed packet-based data with packet switching by each smart radio orother smart apparatus on the path from source to destination. Forexample, each smart radio or other smart apparatus operates as atransmitter, receiver, or transceiver for the local network 204 to servea facility. The smart apparatuses serve as multiple transmit/receivesites interconnected to achieve the range of coverage required by thefacility. Further, the signals on the local networks 204, 208 arebackhauled to a central switch for communication to the cellular towers212, 216.

In embodiments (e.g., in more remote locations), the local network 204is implemented by sending radio signals between smart radios 224. Suchembodiments are implemented in less inhabited locations (e.g.,wilderness) where workers are spread out over a larger work area. Theremay be otherwise inaccessible to commercial cellular service (e.g., fora commercial cellular network, for a cellular-based private network) insuch work areas. An example is where power company technicians areexamining or otherwise working on power lines over larger distances thatare often remote. The embodiments are implemented by transmitting radiosignals from a smart radio 224 a to other smart radios 224 b, 224 c onone or more frequency channels operating as a two-way radio. The radiomessages sent include a header and a payload. Such broadcasting does notrequire a session or a connection between the devices. Data in theheader is used by a receiving smart radio 224 b to direct the “packet”to a destination (e.g., smart radio 224 c). At the destination, thepayload is extracted and played back by the smart radio 224 c via theradio's speaker.

For example, the smart radio 224 a broadcasts voice data using radiosignals. Any other smart radio 224 b within a range limit (e.g., 1 mile(mi), 2 mi, etc.) receives the radio signals. The radio data includes aheader having the destination of the message (smart radio 224 c). Theradio message is decrypted/decoded and played back on only thedestination smart radio 224 c. If another smart radio 224 b receives theradio signals that was not the destination radio, the smart radio 224 bre-broadcasts the radio signals rather than decoding and playing themback on a speaker. The smart radios 224 are thus used as signalrepeaters. The advantages and benefits of the embodiments disclosedherein include extending the range of two-way radios or smart radios 224by implementing radio hopping between the radios.

In some embodiments, the smart radio performs the described radiohopping as an alternative communication method when a facility-specificprivate network, a commercial cellular network (e.g., “public”networks), and other mediums (e.g., Wi-Fi networks) are unavailable. Asdescribed above, the smart radio transitions from using a privatenetwork to a commercial cellular network based on monitoring the smartradio's location relative to geofences for the private network,according to example embodiments. In some embodiments, the smart radiocan transition from the commercial cellular network to radio hoppingbased on geofences for the commercial cellular network, measured signalquality of the commercial cellular network, and/or other criteria. Insome embodiments, the smart radio performs the described radio hoppingfor radio signals transmitted via LMR systems, DMR systems, RoIPsystems, and/or the like. Thus, the smart radio is configured to use atleast one of cellular-based private networks, commercial cellular (e.g.,“public”) networks, and enhanced DMR/LMR/RoIP methods whenever suitable,thereby providing communication reliability and resilience.

In embodiments, the local network is implemented using Radio overInternet Protocol (RoIP). RoIP, is similar to Voice over IP (VoIP), butaugments two-way radio communications rather than telephone calls. Forexample, RoIP is used to augment VoIP with PTT (Push-to-Talk). A smartradio having a PTT button on a user interface 420 is illustrated in FIG.4 . With RoIP, at least one node of a network is a radio (or a radiowith an IP interface device, e.g., the smart radio 224 a) connected viaIP to other nodes (e.g., smart radios 224 b, 224 c) in the local network204. The other nodes can be two-way radios but could also be softphoneapplications running on a smartphone (e.g., the smartphone, or someother communications device accessible over IP). In embodiments, thesmart radio can initiate RoIP voice calls over the local network, and onanother network such as a commercial cellular network, the smart radiocan be used for cellular voice calls, telephone calls, VoIP calls, andthe like.

In embodiments, the local network 204 is implemented using CitizensBroadband Radio Service (CBRS). To enable CBRS, the controller 110includes multiple computing and other devices, in addition to thosedepicted (e.g., multiple processing and memory components relating tosignal handling, etc.). The controller 110 is illustrated and describedin more detail with reference to FIG. 1 . For example, the privatenetwork component 105 (illustrated and described in more detail withreference to FIG. 1 ) includes numerous components related to supportingcellular network connectivity (e.g., antenna arrangements and supportingprocessing equipment configured to enable CBRS). The use of CBRS Band 48(from 3550 MHz to 3700 MHz), in embodiments, provides numerousadvantages. For example, the use of Band 48 provides longer signalranges and smoother handovers. The use of CBRS Band 48 supports numeroussmart radios 224 and smart cameras 228 at the same time. A smartapparatus is therefore sometimes referred to as a Citizens BroadbandRadio Service Device (CBSD).

In alternative embodiments, the Industrial, Scientific, and Medical(ISM) radio bands are used instead of CBRS Band 48. It should be notedthat the particular frequency bands used in executing the processesherein could be different, and that the aspects of what is disclosedherein should not be limited to a particular frequency band unlessotherwise specified (e.g., 4G-LTE or 5G bands could be used). Inembodiments, the local network 204 is a private cellular (e.g., LTE)network operated specifically for the benefit of the facility. Anexample facility 300 implementing a private cellular network usingwireless antennas 374 is illustrated and described in more detail withreference to FIG. 3 . Only authorized users of the smart radios 224 haveaccess to the local network 204. For example, the network 204 uses the900 MHz spectrum. In another example, the local network 204 uses 900 MHzfor voice and narrowband data for land mobile radio (LMR)communications, 900 MHz broadband for critical wide area, long-rangedata communications, and CBRS for ultra-fast coverage of smaller areasof the facility, such as substations, storage yards and office spaces.

In embodiments, the communication systems disclosed herein mitigate thenetwork bottleneck problem when larger groups of workers are working inor congregating in a localized area of the facility. When a large numberof workers are gathered in one area, the smart radios 224 they carry orwear creates too much demand for cellular networks or the cellular tower212 to handle. To solve the problem, in embodiments, the cloud computingsystem 220 is configured to identify when a large number of smart radios224 are located in proximity to each other.

In embodiments, the cloud computing system 220 anticipates wherecongestion is going to occur for the purpose of placing additionalaccess points in the area. For example, the cloud computing system usesthe ML system 2200 to predict where congestion is going to occur basedon bottleneck history and previous location data for workers. An exampleof network choke points are facility entry points where multiple workersarrive in close succession and clock in. The cloud computing system 220accounts for congestion at such entry points by including additionalaccess points at such locations. The cloud computing system 220configures each smart radio 224 a to relay data in concert with theother smart radios 224 b, 224 c. By timing the transmissions of eachsmart radio 224 a, the radio waves from the cellular tower 212 arrive ata desired location, i.e., the desired smart radio 224 a at a differentpoint in time than the point in time the radio waves from the cellulartower 212 arrive at a different smart radio 224 b. Simultaneously, thephased radio signals are overlaid to communicate with other smart radios224 c, mitigating the bottleneck.

The cloud computing system 220 delivers computing services—includingservers, storage, databases, networking, software, analytics, andintelligence—over the Internet (“the cloud”) to offer faster innovation,flexible resources, and economies of scale. FIG. 2A depicts an exemplaryhigh-level cloud-centered network environment 200 otherwise known as acloud-based system. Referring to FIG. 2A, it can be seen that theenvironment centers around the cloud computing system 220 and the localnetworks 204, 208. Through the cloud computing system 220, multiplesoftware systems are made to be accessible by multiple smart radioapparatuses 224, 232, smart cameras 228, 236, as well as more standarddevices (e.g., a smartphone 244 or a tablet) each equipped with localnetworking and cellular wireless capabilities. Each of the apparatuses224, 228, 244, although diverse, embody the architecture of apparatus100 shown by FIG. 1 , but are distributed to different kinds of users ormounted on surfaces of the facility. For example, the smart radio 224 ais worn by employees or independent contracted workers at a facility.The CBRS-equipped smartphone 244 is utilized by an on or off-sitesupervisor. The smart camera 228 is utilized by an inspector or anotherperson wanting to have improved display or other options. Regardless, itshould be recognized that numerous apparatuses are utilized incombination with an established cellular network (e.g., CBRS Band 48 inembodiments) to provide the ability to access the cloud softwareapplications from the apparatuses (e.g., smart radio apparatuses 224,232, smart cameras 228, 236, smart phone 244).

In embodiments, the cloud computing system 220 and local networks 204,208 are configured to send communications to the smart radios 224, 232or smart cameras 228, 236 based on analysis conducted by the cloudcomputing system 220. The communications enable the smart radio 224 orsmart camera 228 to receive warnings, etc., generated as a result ofanalysis conducted. The employee-worn smart radio 224 a (and possiblyother devices including the architecture of apparatus 100, such as thesmart cameras 228, 236) are used along with the peripherals shown inFIG. 1 to accomplish a variety of objectives. For example, workers, inembodiments, are equipped with a Bluetooth enabled gas-detection smartsensor, implemented using the architecture shown in FIG. 1 . The smartsensor detects the existence of a dangerous gas, or gas level. Byconnecting through the smart radio 224 a or directly to the localnetwork 204, the readings from the smart sensor are analyzed by thecloud computing system 220 to implement a course of action due to sensedcharacteristics of toxicity. The cloud computing system 220 sends analert out to the smart radio 224 or smart camera 228, and thus a worker,for example, using speaker 146 or alternative notification means toalert the worker so that they can avoid danger. The speaker 146 isillustrated and described in more detail with reference to FIG. 1 .

Smart Peripheral Apparatuses

In embodiments, a peripheral biometric apparatus implemented using thearchitecture shown by FIG. 1 (e.g., incorporating heart rate, moisturesensors, etc.). The term “peripheral” means that the worker may not berequired to use or carry the particular apparatus unlike the smart radio224 a. For example, the peripheral apparatus uses local network 204and/or the cellular tower 212 to communicate with a biometrics analysissystem. The biometrics analysis system operates on the cloud computingsystem 220 to detect danger indicating biometric conditions of theworker. Heart rates, dehydration, and other biometric parameters aremonitored and analyzed by the cloud computing system 220. Further,warnings are transmitted to the worker through the smart radio 224 a orto anyone else (e.g., a supervisor using apparatus 244) connected withthe overall communication system.

In embodiments, the cloud computing system 220 detects abnormalbiometric conditions using peripheral biometric smart sensors (e.g.,dehydration, abnormally low heart rate). The cloud computing system 220couples the information with readings from a gas-detection smart sensor(e.g., a reading reflecting the presence of hydrogen sulfide gas) toreach a conclusion that the worker needs to immediately get to safety.For example, the biometric and gas-detection devices 116 and 118illustrated and described in more detail with reference to FIG. 1 areused. In embodiments, the cloud computing system 220 uses numerous meansto communicate the warning to the worker. For example, the smart radio224 a includes a vibration warning system that warns the worker byvibration. Or the smart radio 224 a uses the speaker 146 or Bluetoothperipherals illustrated and described in more detail with reference toFIG. 1 .

In embodiments, the smart radio 224 a is repurposed as a camera on sitethat provides video of the site, a node for peer-to-peer communication,and a point of triangulation for device location and identification. Forexample, if the video feed is of lower than suitable quality foridentification of individual workers, the workers are labeled in thevideo based on the smart radio they are carrying. In an example, thesmart radio or cloud computing system 220 operates a facial recognitionsystem (e.g., using the ML system 2200 illustrated and described in moredetail with reference to FIG. 22 ) to perform the labeling. Therepurposed smart radio 224 a provides imaging no matter how the smartradio 224 a is being used. In embodiments, an additional external camera228 is used that is physically separated from the smart radio 224 a viaBluetooth. The smart camera 228 is optionally be used in place ofbuilt-in cameras in the smart radio 224 a or in addition to the built-incameras. The smart radio 224 a would be configured to receive picturestaken by the external camera 228.

In embodiments, the smart radio 224 a is configured to receive photos(e.g., via Bluetooth, another short-range wireless network, the localnetwork 204, or a combination thereof) from other kinds of externalperipheral cameras. For example, the peripheral cameras are wearabledevices such as cameras mounted to glasses or helmets. The peripheralcameras provide a forward-facing view from the perspective of the workerwhile being operated hands-free. Alternatively, a peripheral camera 236is positioned or mounted above a workstation/area, machinery, equipment,or another structure to provide an overhead view or an inside view of acontained area. The peripheral camera 236 provides an internal view ofthe contained area, and is positioned on a gimbal, swivel plate, rail,tripod, stand, post, and/or pole for enabling movement of the camera236. Camera movement is controlled by the worker, under preprogrammedcontrol via controller 110 or via another control mechanism. Inembodiments, multiple views are displayed on display screen 130 frombuilt-in cameras of the peripheral camera 236 (which are represented asone camera 165 in FIG. 1 ). Selection and enhancement (e.g., scrolling,panning, zooming) of views is provided via user-input means 150, forexample. The display screen 130, camera 165, and user-input means 150are illustrated and described in more detail with reference to FIG. 1 .The built-in cameras, in embodiments, are digital-video cameras orhigh-definition digital-video cameras. Optional front and back camerastogether enable the receipt of photo or video content from either sideof the peripheral camera 236.

Machine-Defined Interactions

The cloud computing system 200 uses data received from the smart radioapparatuses 224, 232 and smart cameras 228, 236 to track and monitormachine-defined interactions and collaborations of workers based onlocations worked, times worked, analysis of video received from thesmart cameras 228, 236, etc. An “interaction” describes a type of workactivity performed by the worker. An interaction is measured by thecloud computing system 200 in terms of at least one of a start time, aduration of the activity, an end time, an identity (e.g., serial number,employee number, name, seniority level, etc.) of the worker performingthe activity, an identity of the equipment(s) used by the worker, or alocation of the activity. In embodiments, an interaction is measured bythe cloud computing system 200 in terms of a vector (e.g., [time period1, equipment location 1; time period 2, equipment location 2; timeperiod 3, equipment location 3]). For example, a first interactiondescribes time spent operating a particular machine (e.g., a lathe, atractor, a boom lift, a forklift, a bulldozer, a skid steer loader,etc.), performing a particular task, or working at a particular type offacility (e.g., an oil refinery).

A smart radio 224 a carried or worn by a worker would track that theposition of the smart radio 224 a is in proximity to or coincides with aposition of the particular machine. Example tasks include operating amachine to stamp sheet metal parts for manufacturing side frames, doors,hoods, or roofs of automobiles, welding, soldering, screwing, or gluingparts onto an automobile, all for a particular time period, etc. Alathe, lift, or other equipment, would have sensors (e.g., smart camera228 or other peripheral devices) that log times when the smart radio 224a is in proximity to the equipment and send that information to thecloud computing system 220.

In an example, a smart camera 228 mounted at a stamping shop in anautomobile factory captures video of a worker working in the stampingshop and performs facial recognition or equipment recognition (e.g.,using computer vision elements of the ML system 2200 illustrated anddescribed in more detail with reference to FIG. 22 ). The smart camera228 sends the start time, duration of the activity, end time, identity(e.g., serial number, employee number, name, seniority level, etc.) ofthe worker performing the activity, identity of the equipment(s) used bythe worker, and location of the activity to the cloud computing system220 for generation of one or more interaction(s).

The cloud computing system 220 also has a record of what a particularworker is supposed to be working on or is assigned to for the start timeand duration of the activity. The cloud computing system 220 comparesthe interaction(s) computed with the planned shifts of the worker tosignal mismatches if any. An example interaction describes workperformed at a particular geographic location (e.g., on an offshore oilrig or on a mountain at a particular altitude). The interaction ismeasured by the cloud computing system 200 in terms of at least thelocation of the activity and one of a duration of the activity, anidentity of the worker performing the activity, or an identity of theequipment(s) used by the worker. In embodiments, the machine learningsystem 2200 is used to detect and track interactions, for example,extracting features based on equipment types or manufacturing operationtypes as input data. For example, a smart sensor mounted on the oil rigtransmits to and receives signals from a smart radio 224 a carried orworn by a worker to log the time the worker spends at a portion of theoil rig.

A “collaboration” describes a type of group activity performed by aworker, for example, a group of construction workers working together ina team of two or more in an automobile paint facility, layering achemical formula in a construction site for protection against corrosionand scratches, installing an engine into a locomotive, etc. Acollaboration is measured by the cloud computing system 200 in terms ofat least one of a start time, a duration of the activity, an end time,identities (e.g., serial numbers, employee numbers, names, senioritylevels, etc.) of the workers performing the activity, an identity of theequipment(s) used by the workers, or a location of the activity. Inembodiments, a collaboration is measured by the cloud computing system200 in terms of a vector (e.g., [time period 1, equipment location 1,worker identities 1; time period 2, equipment location 2, workeridentities 2; time period 3, equipment location 3, worker identities3]).

Collaborations are detected and monitored using location tracking (asdescribed in more detail with reference to FIG. 1 ) of multiple smartapparatuses. For example, the cloud computing system 220 tracks andrecords a specific collaboration based on determining that two or moresmart radios 224 were located in proximity to one another within aspecific geofence associated with a particular worksite for apredetermined period of time. For example, a smart radio 224 a transmitsto and receives signals from other smart radios 224 b, 224 c carried orworn by other workers to log the time the worker spends working togetherin a team with the other workers.

In embodiments, a smart camera 228 mounted at a paint facility capturesvideo of the team working in the facility and performs facialrecognition (e.g., using the ML system 2200). The smart camera 228 sendsthe location information to the cloud computing system 220 forgeneration of collaborations. Examples of data downloaded to the smartradios 224 to enable monitoring of collaborations include softwareupdates, device configurations (e.g., customized for a specific operatoror geofence), location save interval, upload data interval, and a webapplication programming interface (API) server uniform resource locator(URL). In embodiments, the machine learning system 2200, illustrated anddescribed in more detail with reference to FIG. 22 , is used to detectand track interactions (e.g., using features based on geographicallocations or facility types as input data).

In embodiments, the cloud computing system 220 determines a “responsetime” metric for a worker. The response time refers to the timedifference between receiving a call to report to a given task and thetime of arriving at a geofence associated with the task. To determinethe response time, the cloud computing system 220 obtains and analyzesthe time the call to report to the given task was sent to a smart radio224 a of the worker from the cloud computing system 220, a local server,or a supervisor's device (e.g., smart radio 224 b). The cloud computingsystem 220 obtains and analyzes the time it took the smart radio 224 ato move from an initial location to a location associated with thegeofence.

In some embodiments, the response time is compared against an expectedtime. Expected time is based on trips originating from a location nearbythe starting location for the worker (e.g., from within a startinggeofenced area, or a threshold distance) and ending at the geofenceassociated with the task, or a regional geofence that the task occurswithin. Embodiments that make use of a machine learning model identifysimilar historical journeys that are similar as a basis of comparison.

In an example, the cloud computing system determines a “repair metric”for a worker and a particular type of equipment (e.g., a power line,etc.) For example, a repair metric identifies how frequently repairs bya given individual were effective. Effectiveness of repairs is machineobservable based on a length of time a given object remains functionalas compared to an expected time of functionality (e.g., a day, a fewmonths, a year, etc.). After a worker is called to repair a givenobject, a timer begins to run. The timer is ended by either of apredetermined period expiring (e.g., expected usable life of repairs) oran additional worker being called to repair that same object.

Thus, where a second worker is called out to fix the same object priorto the expected usable life of the repair has expired, the originalworker is assumed to have done a poor job on the repair and theirrespective repair metric suffers. In contrast, so long as a secondworker has not been called out to repair the same object (as evidencedby location data and dispatch descriptions) during the expectedoperational life of the repairs, the repair metric of the first workerremains positive. The expected operation life of a given set of repairsis based on the object repaired. In some embodiments, a machine learningmodel is used to identify appropriate functional lifetimes of repairsbased on historical examples.

The repair metric is determined by the cloud computing system 200 interms of at least one of locations of the worker (e.g., traveling to theequipment), location of the equipment, time spent in proximity to theequipment, predetermined amount of time the equipment is expected to beoperable (e.g., a day, a few months, a year, etc.) after repair, numberof repairs, etc.

In another example, a repair metric relates to an average amount of timeequipment is operable and in working condition after the worker visitsthe particular type of equipment the worker repaired. The repair metricis determined by the cloud computing system 200 in terms of at least oneof a location of a smart radio 224 a carried by the worker, time spentin proximity to the equipment, predetermined amount of time theequipment is expected to be operable (e.g., a day, a few months, a year,etc.) after repair, or location of the equipment. For example, if theparticular type of equipment is operable for more than 60 days after theworker visited the equipment (to repair it), the repair metric of theworker with respect to the particular type of equipment is increased. Ifthe equipment has broken within less than a week after the workervisited the equipment (to repair it), the repair metric of the workerwith respect to the particular type of equipment is decreased. Inembodiments, the machine learning system 2200, illustrated and describedin more detail with reference to FIG. 22 , is used to detect and trackinteractions (e.g., using features based on equipment types or defectreports as input data).

Another example of a repair metric for a worker relates to a ratio ofthe amount of time an equipment is operable after repair to apredetermined amount of time the equipment is expected to be operable(e.g., a day, a few months, a year, etc.) after repair. Thepredetermined amount of time changes with the type of equipment. Forexample, some industrial components wear out in a few days, while othercomponents can last for years. After the worker repairs the particulartype of equipment, the cloud computing system 220 counts until thepredetermined amount of time for the particular type of equipment isreached. Once the predetermined amount of time is met, the equipment isconsidered correctly repaired, and the repair metric for the worker isincremented. If before the predetermined amount of time, another workeris called to repair the same equipment, the repair metric for the workeris decremented.

In embodiments, equipment is assumed/considered repaired until the cloudcomputing system 220 is informed otherwise. In such embodiments, theworker does not need to wait to receive credit to their repair metric incases where the predetermined amount of time for particular equipment islarge (e.g., months or years).

The smart radio 224 a can track not only the current location of theworker, but also send information received from other apparatuses (e.g.,the smart radio 224 b, the camera 228) to contribute to the recordedlocational information (e.g., of employees 306 at the facility 300 shownby FIG. 3 ). Because the smart radios 224 are readable by the cloudcomputing system 220, locational records can be analyzed to determinehow well the different workers and other device users are doing inperforming various tasks. For example, if a worker is inspecting aparticular vessel in a refinery, it may be necessary for them to spendan hour doing so for a high-quality job to be performed. However, if thelocational data record reveals that the worker was physically at thevessel for only two minutes, it would be an indication of hasty orincomplete work. The cloud computing system 220 can therefore track a“engagement metric” of time spent at a task with respect to the timerequired to be spent for the task to be performed.

In embodiments, the cloud computing system tracks the path chosen by aworker from a current location to a destination as compared to acomputed direct path for determining “route efficiency.” For example,tracking records for multiple workers going from a contractor's buildingat the site to another point within the site can be used to determine(e.g., patterns in foot traffic). In an example, the tracking revealsthat a worker chooses a pathway that causes them to go back and forth toa location on the site that is long and goes around many interferingstructures. The added distances reduce cost-effectiveness because ofwhere the worker is actually walking. Traffic patterns and the “routeefficiency” of a worker monitored and determined by the cloud computingsystem 220 based on positional data obtained from the smart radios 224is used to improve the worker's efficiency at the facility.

In embodiments, the tracking is used to determine whether one or moreworkers are passing through or spending time in dangerous or restrictedareas of the facility. The tracking is used by the cloud computingsystem 220 to determine a “risk metric” of each worker. For example, therisk metric is incremented when time logged by a smart radio that theworker is wearing in proximity to hazardous locations increases. Inembodiments, the risk metric triggers an alarm at an appropriatejuncture. In another example, the facility or the cloud computing system220 establishes geofences around unsafe working areas. Geofencing isdescribed in more detail with reference to FIG. 1 . The risk metric isincremented when the position of the smart radio is determined to bewithin the geofence even though the worker is not supposed to be withinthe geofence for the particular task. In another example, the riskmetric is incremented when a position of the smart radio and sensorsmounted on particular equipment indicate that the equipment is faulty orunsafe to use, yet the worker is using the equipment instead ofsignaling for replacement equipment to be provided. The logged positionand other data are also used to generate records to build an evidenceprofile to be used in accident situations. In some embodiments, theevidence profile includes worker-related events that are temporallyadjacent to an accident scenario. For example, the evidence profileincludes worker events (e.g., interactions with equipment,collaborations with other workers) that occur in a first time windowprior to the time of the accident scenario and in a second time windowafter the time of the accident scenario. Respective spans of the firsttime window and the second time window can be predefined, configuredaccording to a severity level of the accident scenario, and/or the like.Events that are temporally adjacent to an accident scenario can occurwithin a time block or time window in which the accident scenariooccurred. For example, temporally adjacent events include events thatoccurred in the same hour as the accident scenario (e.g., events between2:00 pm and 3:00 pm if the scenario also occurred between 2:00 pm and3:00 pm), events that occurred in a same worker shift as the accidentscenario (e.g., events between 12 pm and 5:00 pm if the scenariooccurred during an afternoon shift defined between 12 pm and 5:00 pm),and/or the like.

In embodiments, the established geofencing described herein enables thesmart radio 224 a to receive alerts transmitted by the cloud computingsystem 220. The alerts are transmitted only to the apparatuses worn byworkers having a risk metric above a threshold in this example. Based onlocational records of the apparatuses connected to the local network204, particular movable structures within the refinery may be moved suchthat a layout is configured to reduce the risk metric for workers in therefinery (e.g., where the cloud computing system 220 detects thatemployees are habitually forced to take longer walk paths in order toget around an obstructing barrier or structure). In embodiments, the MLsystem 2200 is used to configure the layout to reduce the risk metricbased on features extracted from coordinates of the geofencing, storedrisk metrics, the locational records of the apparatuses connected to thelocal network 204, locations of the movable structures, or a combinationthereof.

The cloud computing system 220 hosts the software functions to trackoperations, interactions, collaborations, and repair metrics (which aresaved on one or more databases in the cloud) to determine performancemetrics and time spent at different tasks and with different equipment,generate work experience profiles of frontline workers based oninterfacing between software suites of the cloud computing system 220and the smart radio apparatuses 224, 232, smart cameras 228, 236, smartphone 244. The cloud computing system 200 is, in embodiments, configuredby an administrating organization to enable workers to send and receivedata to and from their smart devices. For example, functionality desiredto create an interplay between the smart radios and other devices withsoftware on the cloud computing system 220 is configured on the cloud byan organization interested in monitoring employees, transmitting alertsto these employees based on determinations made by a local server or thecloud computing system 220. Amazon Web Services (AWS), Microsoft Azure,and Google Cloud are widely used examples of a cloud platform, butothers could be used instead.

Tracking of interactions, collaborations, and repair metrics isimplemented in, for example, Scheduling Systems (SS), Field DataManagement (FDS) systems, and/or Enterprise Resource Planning (ERP)software systems that are used to track and plan for the use of facilityequipment and other resources. Manufacturing Management System (MMS)software is used to manage the production and logistics processes inmanufacturing industries (e.g., for the purpose of reducing waste,improving maintenance processes and timing, etc.) Risk Based Inspection(RBI) software assists the facility using optimizing maintenancebusiness processes to examine equipment and/or structures, and trackinteractions, collaborations, and repair metrics prior to and after abreakdown in equipment, detection of manufacturing failures, ordetection of operational hazards (e.g., detection of gas leaks in thefacility). The amount of time each worker logs at an interaction,collaboration, or other machine-defined activity with respect todifferent locations and different types of equipment is collected andused to update an “experience profile” of the worker on the cloudcomputing system 220 in real-time. The repair metric and engagementmetric for each worker with respect to different locations and differenttypes of equipment is collected and used to update the experienceprofile of the worker on the cloud computing system 220 in real-time.

Experience Profile Features

FIG. 2B is a flow diagram illustrating an example process for generatinga work experience profile using apparatuses 100, 242 a, 242 b, andcommunication networks 204, 208 for device tracking and geofencing, inaccordance with one or more embodiments. The apparatus 100 isillustrated and described in more detail with reference to FIG. 1 . Thesmart radios 224 and local networks 204, 208 are illustrated anddescribed in more detail with reference to FIG. 2A. In embodiments, theprocess of FIG. 2B is performed by the cloud computing system 220illustrated and described in more detail with reference to FIG. 2A. Inembodiments, the process of FIG. 2A is performed by a computer system,for example, the example computer system 2300 illustrated and describedin more detail with reference to FIG. 23 . Particular entities, forexample, the smart radios 224 or the local network 204, perform some orall of the steps of the process in embodiments. Likewise, embodimentscan include different and/or additional steps, or perform the steps indifferent orders.

The experience profile that is automatically generated and updated bythe cloud computing system 220 in real-time includes multiple profilelayers that store a record of work history of the worker. Inembodiments, an HR employee record is created that lists what eachworker was doing during a particular shift, at a particular location,and at a particular facility to build an evidence profile to be used inaccident situations. For example, the cloud computing system 220automatically generates or builds an evidentiary data log for anaccident event, and said log can be used for enhancing accidentreporting, streamlining accident investigation, supporting subjectivehuman statements, and the like. A portion of the data in the experienceprofile can follow a worker when they change employment. A portion ofthe data remains with the employer.

In step 272, the cloud computing system 220 obtains locations and timelogging information from multiple smart apparatuses (e.g., smart radios224) located at a facility. An example facility 300 is illustrated anddescribed in more detail with reference to FIG. 3 . The locationsdescribe movement of the multiple smart apparatuses with respect to thetime logging information. For example, the cloud computing system 220track of shifts, types of equipment, and locations worked by eachworker, and uses the information to develop the experience profileautomatically for the worker, including formatting services. When theworker joins an employer or otherwise signs up for the service, relevantpersonal information is obtained by the cloud computing system 220 toestablish payroll and other known employment particulars. The workeruses a smart radio 224 a to engage with the cloud computing system 220and works shifts for different positions. In embodiments, the cloudcomputing system 220 performs incident mapping based on the locations,time-logging information, shifts, types of equipment, etc. For example,the cloud computing system 220 determines where the worker was withrespect to an accident when the accident occurred, and a timeline of theworker's locations before and after the accident. The incident mappingand the timeline is used to augment the risk metric described herein. Inparticular, the cloud computing system 220 determines worker events thatare temporally adjacent to the accident, or occurring immediately beforeor after the accident, occurring within a pre-defined or configured timewindow of the accident, and/or the like.

In step 276, the cloud computing system 220 determines interactions andcollaborations for a worker based on the locations and the time logginginformation. Interactions and collaborations are described in moredetail with reference to FIG. 2A. The interactions describe workperformed by the worker with equipment of the facility (e.g., lathes,lifts, crane, etc.) The collaborations describe work performed by theworker with other workers of the facility. The cloud computing system220 tracks the shifts worked, the amount of time spent with differentequipment, interactions, collaborations, the relevant skills withrespect to those shifts, etc. In some embodiments, the cloud computingsystem 220 tracks and stores the interactions and collaborations. In anexample, the cloud computing system 220 stores a window of interactionsand collaborations determined or detected, for example, in the lastmonth, the last week, the last day, or the last hour. The cloudcomputing system 220 can refer to stored interactions and collaborationswhen later generating profiles related to a worker, for example, anexperience profile or an accident report/log. In some embodiments, thecloud computing system 220 determines past interactions andcollaborations (e.g., by command in response an occurrence of anaccident event) at certain past timepoints based on the locations andtime logging information being stored.

The cloud computing system 220 generates a format for the experienceprofile of the worker based on the interactions and collaborations. Thecloud computing system 220 generates the format by comparing theinteractions and collaborations with respect to types of work performedby the worker with the equipment and the other workers. In an example,the cloud computing system 220 analyzes machine observations, such aslocation tracing of a smart radio a worker is carrying over a specificperiod of time cross-referenced with known locations of equipment.

In another example, the cloud computing system 220 analyzescontemporaneous video data that indicates equipment location. Themachine observations used to denote interactions and collaborations aredescribed in more detail with reference to FIG. 2A, for example, a starttime, a duration of the activity, an end time, identities of the workersperforming the activity, identity of the equipment(s) used by theworkers, or a location of the activity.

The cloud computing system 220 assembles the information collected andidentifies a format for the experience profile. The format is based onthe information collected. Where a given worker has workedpositions/locations with many different employers (as measured bythreshold values), the format focuses on the time spent at the differenttypes of work as opposed to individual employment. Where a worker hasspent most of their time at a few specialized jobs (e.g., welding), theexperience profile format is tailored toward employment that is relatedto that skill and deemphasizes unrelated employment (e.g., where theworker is a welder, time spent as a truck driver is not particularlyrelevant).

Where a given worker has worked on many (as measured by thresholds)shifts repeatedly with a given type of equipment, the experience profileformat focuses on the worker's relationship with the given equipment.Based on the automated analysis, the system procedurally generates theexperience profile content (e.g., descriptions of skills or attributes).The cloud computing system 220 includes multiple format templates thatfocus on emphasizing parts of the worker's experience profile or targetjobs. Additional format templates are added based on evolving styles invarious industries.

In embodiments, template styles are identified via the ML system 2200.In step 280, the cloud computing system 220 extracts a feature vectorfrom the interactions and collaborations using an ML model. Examplemeasures that the cloud computing system 220 uses to denote interactionsby are described in more detail with reference to FIG. 2A, for example,a start time, a duration of the activity, an end time, identities of theworkers performing the activity, identity of the equipment(s) used bythe workers, or a location of the activity. The feature vector would beextracted from the measures. An example ML system 2200, example featurevector 2212, and an example ML model 2216 are illustrated and describedin more detail with reference to FIG. 22 . The feature vector describestypes of work performed by the worker with the equipment and the otherworkers.

In step 284, the cloud computing system generates a format for anexperience profile of the worker based on the feature vector using theML model. The ML model is trained, based on stored experience profiles,to identify a format template for the format. The format includesmultiple fields. To train the ML system 2200, information from storedexperience profiles is input into the ML system 2200. The ML system 2200interprets what appears on those stored experience profiles andcorrelates content of the worker's experience profile (e.g., time loggedat particular experiences) to structure (e.g., how the experienceprofile is written). The ML system 2200 uses the worker's experienceprofile as compared to the data structures based on the training data toidentify what elements of the worker's experience profile are the mostrelevant.

Similarly, the ML system 2200 identifies what information tends to notappear together and filters lower incidence data out. For example, whena worker has many (as measured by thresholds) verified or confirmedhours working with particular equipment, then experience at unskilledlabor will tend not to appear on the worker's experience profile. In theexample, the “lower incidence” data is the experience relating tounskilled work; however, the lower incidence varies based on thetraining data in the ML system 2200. The relevant experience data thatis not filtered out is based on the experience profile content thattends to appear together across the training set. The population of thetraining set is configured to be biased toward particular traits (e.g.,hours spent using complex equipment) by including more instances ofexperience profiles having complex equipment listed than non-skilledwork.

For example, the listed work experience in the experience profileincludes 350 hours spent working on an assembly system for injectionvalves or 700 hours spent driving an industrial lift jack system havinghydraulic rams with a capacity of 1000 tons. Such work experience iscollated by the ML system 2200 from location data of the worker, sensordata of the equipment, shift data, etc. In embodiments, especiallyembodiments relying upon the ML system 2200, a specific format templateis not used. Rather, the ML system 2200 identifies a path in anartificial neural network where the generated experience profile contentadheres to certain traits or rules that are template-like in natureaccording to that path of the neural network.

In step 288, the cloud computing system 220 generates the experienceprofile by filling the multiple fields of the format with informationdescribing the interactions, the collaborations, repair metrics of theworker describing history of repairs to the equipment by the worker, andengagement metrics of the worker describing time spent by the workerworking on the equipment. Repair metrics and engagement metrics aredescribed in more detail with reference to FIG. 2A. The cloud computingsystem 220 automatically fills in fields/page space of the experienceprofile format identified. The data filled into the field space of theexperience profile includes the specific number of hours that a workerhas spent working with a particular type of equipment (e.g., 200 hoursspent driving forklifts, 150 hours spent operating a lathe, etc.)Details used to fill in the format fields favor more recent experiences,interactions, and collaborations, or employment having stronger repairmetrics and engagement metrics. In embodiments, the experience profilecontent is generated via procedural rules and predefined format templatestructures.

In embodiments, the cloud computing system 220 exports or publishes theexperience profile to a user profile of a social or professionalnetworking platform (e.g., such as LinkedIn™, Monster™, any othersuitable social media or proprietary website, or a combination thereof).In embodiments, the cloud computing system 220 exports the experienceprofile in the form of a recommendation letter or reference package topast or prospective employers. The experience data enables a givenworker to prove that they have a certain amount of experience with agiven equipment platform.

In some embodiments, the cloud computing system 220 additionally, oralternatively, builds an evidentiary data log for an accident event fromthe interactions and collaborations detected in step 276. According toan example method, the cloud computing system 220 automatically buildsan evidentiary data log for an accident event, in response to adetection of an accident event based on sensor data collected by thecloud computing system from sensor devices located throughout thefacility. The evidentiary data log identifies, for the worker, at leastone first worker-equipment interaction or first worker-workercollaboration that occurred temporally adjacent to the accident event.In some examples, the cloud computing system can adjust a risk metric ofthe worker based on the interactions and the collaborations. In someembodiments, the evidentiary data log includes a timeline of the trackedlocations of the worker (or the smart radio associated with the worker)before and after the accident event. In some embodiments, the cloudcomputing system 220 provides a dynamic visualization of the timeline oftracked locations according to embodiments described herein.

Data pertaining to a given worker is organized into multiple tiers. Insome embodiments, the tiers are structured into an individual basis, asconnected to the contract they are working, and as connected to theiremployer. Each of those tiers operates identity management within thecloud computing system 220. When a worker ceases to work for an employeror cease to work on a contract, their individual data (e.g., theirtraining, what they did, risk metrics determined by the cloud computingsystem for the worker) continues to follow them through the system tothe next employer/contract they are attached to. Data is conserved inescalating tiers such that individual data is stored to the contractlevel and stored to the employer level.

Conversely, data pertaining to the contract (e.g., performance data,hours worked, accident mapping) stays with the contract tier. Forexample, the cloud computing system associates portions of anevidentiary data log for an accident with different persistence levelsthat control whether the portions of the evidentiary data log remainassociated with the worker subsequent to the worker no longer beingassociated with the facility or a current employer. With respect toexamples in which accident mapping stays with the contract tier, certainportions of an evidentiary data log can be associated with a persistencelevel to cause the certain portions to be disassociated from the workersubsequent to the worker no longer being associated with the currentemployer. Similarly, data pertaining to the employer tier (e.g., thesame as contract data across multiple contracts) remains with theemployer. For example, the cloud computing system enables access to thecertain portions of an evidentiary data log by current users associatedwith the facility and the employer, even after the worker is no longerassociated with the employer.

Users are part of a global directory of login profiles to the smartradios (or other interface platforms). Regardless of whichemployer/facility/project/other group delineation the user is associatedwith, the user logs in to the smart radio using the same login identity.The global directory enables traceability of otherwise transientworkers. The global directory improves efficiency or emergency responseby enabling quicker decision making and also allowing differentpermissions in different facilities for the same user. Each user has aseamless experience in multiple facilities and need not worry aboutmultiple passwords per group delineation.

FIG. 3 is a drawing illustrating an example facility 300 usingapparatuses and communication networks for device tracking andgeofencing, in accordance with one or more embodiments. For example, thefacility 300 is a refinery, a manufacturing facility, a constructionsite, etc. An example apparatus 100 is illustrated and described in moredetail with reference to FIG. 1 . The communication technology shown byFIG. 3 is implemented using components of the example computer system2300 illustrated and described in more detail with reference to FIG. 23.

Multiple differently and strategically placed wireless antennas 374 areused to receive signals from an Internet source (e.g., a fiber backhaulat the facility), or a mobile system (e.g., a truck 302). The wirelessantennas 374 is similar to or the same as the wireless antenna 174illustrated and described in more detail with reference to FIG. 1 . Thetruck 302, in embodiments, includes the edge kit 172 illustrated anddescribed in more detail with reference to FIG. 1 . The strategicallyplaced wireless antennas 374 repeat the signals received and sent fromthe edge kit 172 such that a private cellular network (e.g., the localnetwork 204) is made available to multiple workers 306. Each workercarries or wears a cellular-enabled smart radio. The smart radio isimplemented using the apparatus 100 illustrated and described in moredetail with reference to FIG. 1 . As described in more detail withreference to FIG. 1 and FIG. 2A, a position of the smart radio iscontinually tracked during a work shift.

In implementations, a stationary, temporary, or permanently installedcellular (e.g., LTE or 5G) source (e.g., edge kit 172) is used thatobtains network access through a fiber or cable backhaul. Inembodiments, a satellite or other Internet source is embodied intohand-carried or other mobile systems (e.g., a bag, box, or otherportable arrangement). A backhaul arrangement such as the cellular orother Internet source provides access to the cloud computing system viathe private network (e.g., local network 204). FIG. 3 shows thatmultiple wireless antennas 374 are installed at various locationsthroughout the facility. Where the edge kit 172 is located at a locationnear a facility fiber backhaul, the communication system in the facility300 uses multiple omnidirectional Multi-Band Outdoor (MBO) antennas asshown. Where the Internet source is instead, located near an edge of thefacility 300, as is often the case, the communication system uses one ormore directional wireless antennas to improve the coverage in terms ofbandwidth. Alternatively, where the edge kit, if in a mobile vehicle,for example, truck 302, the antennas' directional configuration would bepicked depending on whether the vehicle would ultimately be located at acentral or boundary location.

In embodiments where a backhaul arrangement is installed at the facility300, the edge kit 172 is directly connected to an existing fiber router,cable router, or any other source of Internet at the facility. Inembodiments, the wireless antennas 374 are deployed at a location inwhich the apparatus 100 (e.g., a smart radio) is to be used. Forexample, the wireless antennas 374 are omnidirectional, directional, orsemi-directional depending on the intended coverage area. Inembodiments, the wireless antennas 374 support a local cellular network(e.g., the local network 204 illustrated and described in more detailwith reference to FIG. 2A). In embodiments, the local network is aprivate LTE network (e.g., based on 4G or 5G). In more specificembodiments, the network is a Band 48 Citizen's Broadband Radio Service(CBRS) local network. The frequency range for Band 48 extends from 3550MHz to 3700 MHz and is executed using Time Division Duplexing (TDD) asthe duplex mode. The private LTE wireless communication device 105(illustrated and described in more detail with reference to FIG. 1 ) isconfigured to operate in the private network created, for example,configured to accommodate Band 48 CBRS in the frequency range for Band48 (again, from 3550 MHz to 3700 MHz) and accommodates TDD. Thus,channels within the preferred range are used for different types ofcommunications between the cloud and the local network.

FIG. 4 is a drawing illustrating example apparatuses for device trackingand geofencing, in accordance with one or more embodiments. Theapparatuses shown by FIG. 4 are smart radios. The smart radios areimplemented using components of the example computer system 2300illustrated and described in more detail with reference to FIG. 23 .

The features of the smart radio include an easy to grab volume controldial that can be used to, with one hand, increase or decrease the volumeof the device as well as a push-to-talk button 420. The volume controlcontrols the loudness of the smart radio (e.g., the speaker of the audiodevice 146 illustrated and described in more detail with reference toFIG. 4 ), while the push-to-talk button 420, when depressed, enablesvoice transmissions/messages to be sent to other smart device (e.g., thesmart camera 228 illustrated and described in more detail with referenceto FIG. 2A). Electronic circuits in the controller 110 enable signalsfrom the push-to-talk button 420 and the volume control to result in thedesired functions. The controller 110 is illustrated and described inmore detail with reference to FIG. 1 .

FIG. 5 is a drawing illustrating example apparatuses for device trackingand geofencing, in accordance with one or more embodiments. A user-inputsystem is implemented on the smart radios (illustrated in more detail inFIG. 4 ) for receiving user inputs and transmitting the user inputs tocontroller 110. The controller 110 is illustrated and described in moredetail with reference to FIG. 1 . User inputs include any user-inputmeans including but not limited to touch inputs, audible commands, akeyboard, etc. In the embodiments of the smart radio depicted in FIG. 5, a user-input device includes multiple navigational tools that areoperable by the finger/thumb of a worker. As depicted in FIG. 5 , thenavigational tools include a down navigational button 512, an upnavigational button 508, a selection button 516, and a back/home button504. In some embodiments, the down and up navigational buttons 508, 512are constructed in a concave arrangement to enable gloved hands to morereadily identify the bounds of each button.

To enable operation of the buttons and other navigational means of thesmart radio by a worker wearing work gloves, the buttons describedherein click at a predetermined force/psi. The predetermined force/psiis selected such that a heavy touch by a gloved finger or hand will notresult in multiple clicks and that a touch will not depress multiplebuttons. In some embodiments, force- or pressure-sensitive operation ofthe buttons is implemented using hardware features included in theuser-input system. For example, the user-input system includes one ormore of springs, switches, rubber rings or drums, elastic resistance,and/or the like that cause a button to not fully depress and provideresultant input to a controller until at least the predetermined forceor pressure is used. In some embodiments, the user-input system includessensing devices, such as force or pressure sensors, that provide forceor pressure measurements based on which inputs via the buttons areprovided to a controller or not (e.g., via an operational amplifierconfigured as a comparator, via operating system level software, and/orthe like).

The down navigational button 512 and up navigational button 508 enablescrolling up or down through displayed content, and the outwardlyextending selection button 516 is depressible to select menu options.The back/home button 504 enables a worker to back out of selectedoptions and ultimately to return to a home screen. The other handhelddevices (e.g., smart camera 228 illustrated and described in more detailwith reference to FIG. 2A) will use other kinds of arrangements (e.g., atouchscreen, or other buttons) without departing from the scope hereof.An example text messaging display 240 is illustrated in FIG. 2A.

In embodiments, the buttons shown by FIG. 5 or other user-input means ofthe smart radio disclosed include capacitive sensors to disable thebuttons and other input means when pressed by or in contact with barehuman skin. The benefits of the embodiments include prevention of use ofthe smart radio or other smart apparatus by a worker who is not suitablygloved for work. For example, for worksite safety, the back/home button504 is rendered inoperable by a Touch ID sensor when depressed by a barehand or finger. In particular, the capacitive sensors measure acapacitance of an object in contact with a given pressure-sensitivebutton and disables the input of the given pressure-sensitive button ifthe measured capacitance is indicative of bare, ungloved human skin.

Charging Station Features

FIG. 6 is a drawing illustrating example charging cradles forapparatuses implementing device tracking and geofencing, in accordancewith one or more embodiments. An example charging cradle 800 is shown byFIG. 8 . For example, the smart radio depicted by FIG. 4 is removed outof a charging cradle by a worker clocking in at a facility and placed inthe charging cradle by the worker clocking out of the facility. Anexample facility 300 is illustrated and described in more detail withreference to FIG. 3 . The charging cradles are arranged in arrays andmounted on a surface at an entry or exit of the facility. Multiple smartradios are placeable in an array of charging cradles as shown by FIG. 7. An example two-dimensional array of charging cradles 900 is shown byFIG. 9 .

In embodiments, a charging cradle provides a simplified way to plug-inthe smart radio disclosed herein hot, cold, or standby. In a cold dockor undock, a worker shuts down or powers off the smart radio beforedocking/undocking. In a hot dock or undock, the smart radio remainsrunning when docked/undocked. In standby docking or undocking, the smartradio is docked/undocked while powered on but requires that it be placedinto a sleep mode prior to docking/undocking.

FIG. 7 is a drawing illustrating example charging cradles forapparatuses implementing device tracking and geofencing, in accordancewith one or more embodiments. The charging cradles are shown, eachhaving a smart radio inserted into the charging cradle. Each smart radioinventories an NFC tag or card embedded in or otherwise located within arespective charging cradle. An example charging cradle 800 is shown byFIG. 8 . Examples of an NFC rubber pad 804 and an NFC tag 808 located oncharging cradles are also depicted in more detail in FIG. 8 . Forexample, the smart radio connects to the NFC tag 804, and the particularNFC tag 804 enables the smart radio to communicate its location to thecloud computing system 220 based on the known location of the particularNFC tag 808 of the particular charging cradle. The NFC technology usedby the smart radios is described in more detail with reference to FIG. 1. The cloud computing system 220 is described in more detail withreference to FIG. 1 . An example two-dimensional array of chargingcradles 900 is shown by FIG. 9 .

FIG. 8 is a drawing illustrating example charging cradles forapparatuses implementing device tracking and geofencing, in accordancewith one or more embodiments. In embodiments, the smart radioillustrated and described in more detail with reference to FIG. 4includes a charging port, which enables it to be received into acharging cradle 800.

An example NFC rubber pad 804 and NFC tag 808 located on chargingcradles are depicted in FIG. 8 .

The smart radio connects to only one NFC tag at a time, reducingaccidental transactions. In embodiments, encrypted data exchange happensbetween the NFC tag 808 and the smart radio. The NFC tag 808 and thesmart radio connect instantly for data exchange when brought closetogether or when the smart radio is placed in the charging cradle. Thesmart radio has an NFC module that connects wirelessly and without anexternal power source. The nearby connection is limited to one smartradio and protects the data exchange from remote jacking by a maliciousentity.

FIG. 9 is a drawing illustrating example charging cradles forapparatuses implementing device tracking and geofencing, in accordancewith one or more embodiments. An example charging cradle 800 is shown byFIG. 8 . The charging cradles shown in FIG. 7 link together in aparticular manner using magnets embedded in the base or sides of thecradles, forming a two-dimensional array (as shown by FIG. 9 ). Inembodiments, the charging cradles are mounted (e.g., sideways) to a wallof the facility, or (e.g., facing up) on a horizontal surface of thefacility. An example facility 300 is illustrated and described in moredetail with reference to FIG. 3 .

FIG. 10 is a drawing illustrating example drainage holes for chargingcradles for apparatuses implementing device tracking and geofencing, inaccordance with one or more embodiments. An example charging cradle 800is shown by FIG. 8 . Each charging cradle include a drainage hole forletting water or other liquids run off wet devices that are placed inthe charging cradle. The drainage holes are shaped and otherwisephysically configured to enable the water runoff whether the chargingcradle is positioned facing up (e.g., when the charging cradle ismounted on a horizontal surface) or facing sideways (e.g., when wallmounted).

Location-Based Features

As described herein, smart radios are configured with locationestimating capabilities and are used within a facility or worksite forwhich geofences are defined. A geofence refers to a virtual perimeterfor a real-world geographic area, such as a portion of a facility orworksite. A smart radio includes location-aware devices (e.g., positiontracking component 125, position estimating component 123) that informof the location of the smart radio at various times. Embodimentsdescribed herein relate to location-based features for smart radios orsmart apparatuses. Location-based features described herein use locationdata for smart radios to provide improved functionality. In someembodiments, a location of a smart radio (e.g., a position estimate) isassumed to be representative of a location of a worker using orassociated with the smart radio. As such, embodiments described hereinapply location data for smart radios to perform various functions forworkers of a facility or worksite.

Responder-Targeted Communications

Some example scenarios that require radio communication between workersare area-specific, or relevant to a given area of a facility. As oneexample, a local hazardous event in a given area of a facility is nothazardous to other workers in other areas that are remote. As anotherexample, a downed (e.g., injured, disabled) worker in a given area of afacility requires immediate assistance and that attention is unlikely tobe provided from other workers in other areas. The use of geofences todefine various areas within a facility or worksite provides a means fordefining area-specificity of various scenarios and events.

Radio communication with workers located in a given area is needed tohandle area-specific scenarios relevant to the given area. In someexamples, the communication is needed at least to transmit alerts tonotify the workers of the area-specific scenario and to conveyinstructions to handle and/or remedy the scenario.

According to some embodiments, locations of smart radios are monitored(e.g., by cloud computing system 220) such that at a point in time, eachsmart radio located in a specific geofenced area is identified. FIG. 11illustrates an example of a worksite 1100 that includes a plurality ofgeofenced areas 1102, with smart radios 1105 being located within thegeofenced areas 1102.

In some embodiments, an alert, notification, communication, and/or thelike is transmitted to each smart radio 1105 that is located within ageofenced area 1102 (e.g., 1102C) responsive to a selection orindication of the geofenced area 1102. A smart radio 1105, anadministrator smart radio (e.g., a smart radio assigned to anadministrator), or the cloud computing system 220 is configured toenable user selection of one of the plurality of geofenced areas 1102(e.g., 1002C). For example, a map display of the worksite 1100 and theplurality of geofenced areas 1102 is provided. With the user selectionof a geofenced area 1102 and a location for each smart radio 1105, a setof smart radios 1105 located within the geofenced area 1102 isidentified. An alert, notification, communication, and/or the like isthen transmitted to the identified smart radios 1105.

However, in various examples, technical challenges arise with masscommunication with each worker located in a given area. That is, despitean area-specific scenario potentially being relevant to each worker,communication with all workers located in the area requires asignificant amount of resources and time. For example, in theillustrated example of FIG. 11 , the geofenced area 1102C includes fivesmart radios. Inefficiencies and delays in response time arise whencommunication with all five smart radios is attempted. Further, ifcontinued communication is needed following an initial alert ornotification, not all workers are guaranteed to have seen and read theinitial alert or notification. Thus, in some examples, repetition ofinformation redundant with an initial communication is needed forworkers who have not actually seen the initial communication.Additionally, with different geofenced areas 1102 having a differentnumber of smart radios 1105, area-wide communication for different areasbecomes inconsistent and potentially unreliable.

Accordingly, embodiments described herein provide response-orderedcommunication with local smart radios to address at least theseidentified technical challenges. In particular, example embodimentsestablish communications with a selected subset of smart radios 1105located within a geofenced area 1102C. The subset of smart radios 1105is selected based on a response time to an initial communicationtransmitted to each of a superset of smart radios within the geofencedarea 1102C.

As such, example embodiments enable efficient and rapid handling ofarea-specific scenarios due to the selection of smart radios based onresponse time. Smart radios with responsive behavior are selected, whichresults in continued communication with workers who are adequatelyinformed and prepared to handle the area-specific scenario. This resultsin communication resources not being spent on non-selected smart radioswhose workers are delayed in being informed of the area-specificscenario (e.g., workers that are busy and occupied with other matters).

An illustrative non-limiting example is described with reference to FIG.11 , and the geofenced area 1102C with five smart radios. As discussedabove, inefficient operational delays occur with communicating via eachof the five smart radios. For example, a given worker is occupied anddistracted by another task and fails to become aware of an emergencythat is alerted via a smart radio. As such, the given worker is notadequately prepared or briefed for continued communication to allow forresponding to and handling the emergency. Establishing the continuedcommunications with the otherwise occupied worker would result ininefficiencies in the response and handling of the emergency.

Accordingly, a subset of the five smart radios are selected based onresponse time to an initial communication transmitted to each of thefive smart radios. For example, the first two smart radios to respond byperforming an activity related to the initial communication areselected. As another example, smart radios that perform an activitywithin a threshold time of the initial communication are selected.

That is, response time refers to a time that passes before a smart radioperforms an activity related to and/or in response to an initialcommunication. In some embodiments, response time is measured as a timespanning between when the initial communication is received by the smartradio and when an activity is detected at the smart radio.

In some embodiments, the activities at a smart radio that controlresponse time are related to user interactions by a worker with thesmart radio. For example, response time is determined based on when aworker reads the initial communication. In an example, the reading ofthe initial communication is detected based on the initial communicationbeing displayed for a threshold amount of time. In another example, thereading of the initial communication is detected based on a display ofthe initial communication being initiated (e.g., responsive to a userinteraction with a displayed notification of the initial communication).In yet another example, the reading of the initial communication isdetected based on a threshold degree of movement or jostling that ismeasured via a gyroscope, an accelerometer, and/or similar sensors onthe smart radio.

As another example, response time is determined based on a responsetransmitted by the smart radio. For example, the response time isdetermined based on the smart radio transmitting an acknowledgement, areceipt, and/or the like back to an administrator smart radio from whichthe initial communication was transmitted. In an example, theacknowledgement, receipt, and/or the like is transmitted in response toa command from the worker. As such, the acknowledgement, receipt, and/orthe like is representative of the initial communication reaching theworker.

These and other example activities are detected and used to determineresponse times for different smart radios. As discussed, smart radioswith short response times (e.g., compared to other smart radios, withina threshold time) are selected, and further communication is establishedwith the selected smart radios. For example, a communication channel(e.g., a video call, an audio call, a text conversation or thread) isinitiated between the administrator smart radio and the selected smartradio(s).

Accordingly, an administrator is able to communicate further details andinstructions to worker(s) at the selected smart radio(s) via theinitiated communication channel. The worker(s) is likely to have seenthe initial communication and have an initial informed awareness of anarea-specific scenario. The administrator does not need to repeatinformation and directly communicate further details or instructions,thus saving critical time needed to handle and respond to scenarios inthe facility. As such, technical benefits are provided by establishingcommunications with a first responder audience selected from a localizedpopulation of workers.

Turning now to FIG. 12 , a flow diagram is provided. The flow diagramillustrates an example process for response-controlled communicationsfor geofenced areas. In some examples, the illustrated process isperformed to minimize resource usage when communicating with workers ina facility about local scenarios and events. In some embodiments, theillustrated process is performed by a cloud computing system 220 (e.g.,shown in FIG. 2A). In some embodiments, the illustrated process isperformed by a computer system, for example, the example computer system2300 illustrated and described in more detail with reference to FIG. 23. Particular entities, for example, the smart radios (e.g., smart radios1105, smart radios 224), perform some or all of the steps of the processin some embodiments. Likewise, some embodiments include different and/oradditional steps, or perform the steps in different orders.

In step 1202, a plurality of smart apparatuses (e.g., smart radios 1105,smart radios 224) located within a geofenced area are identified. Insome embodiments, the smart apparatuses are identified based onobtaining location and time logging information from multiple smartapparatuses. Locations of the multiple apparatuses are mapped to aplurality of geofences that define areas within a worksite, such as theexample geofenced areas illustrated in FIG. 11 .

In some embodiments, step 1202 is performed in response to a selectionor an indication of the geofenced area. In an example, a geofenced arearelevant to a detected event or scenario is automatically identified andused to identify the plurality of smart apparatuses.

In step 1204, a first communication is transmitted to the plurality ofsmart apparatuses that are identified as being located within thegeofenced area. In some embodiments, the first communication is atext-based alert or notification of an event or scenario that isrelevant and specific to the geofenced area. In some embodiments, thefirst communication is an audio-based and/or video-based message that isbroadcast to the plurality of smart apparatuses.

In an example, the first communication is broadcast to workersassociated with the plurality of smart apparatuses via localinfrastructure located in the geofenced area, such as intercoms, alarms,video screens or billboard-like structures, and/or the like.

In step 1206, a subset of the plurality of smart radios is selected. Insome embodiments, the subset of smart radios is selected according tothe detection of response activities at the smart radios and accordingto response times based on the detection of response activities.Accordingly, the subset of smart radios constitutes a first responderaudience. The subset of smart radios represents a subset of workers whoresponded to the initial communication in a manner that satisfiesvarious constraints or thresholds.

For example, the subset of smart radios is selected according to aresponse time threshold. Smart radios at which a response activity isdetected before the response time threshold are selected for the subset.As another example, the smart radios are ordered according to respectivetimes at which response activities are detected. A first number of firstradios in the order are selected for the subset.

In some embodiments, additional constraints or thresholds are consideredwhen selecting the subset of smart radios. For example, smart radios areassigned to different workers with different roles, role levels,profiles, and/or the like. Smart radios whose assigned worker satisfiesa threshold role level, a role/profile requirement, and/or the like areconsidered for the selection of the subset. In some embodiments, theadditional constraints (e.g., threshold role level, role requirement)are determined based on the relevant event or scenario that prompted theprocess.

In step 1208, a communication channel with the subset of smart radios isautomatically established. In some embodiments, the communicationchannel is established between the subset of smart radios and thecomputer system performing the process, such as an administratorcomputer system. In some embodiments, the communication channel isestablished between the subset of the smart radios and an administratorsmart radio. In some embodiments, the communication channel isestablished between the smart radios of the subset to enable the localworkers to coordinate the handling of and response to the relevant eventor scenario. In some embodiments, the communication channel is a videocall, an audio call, a text conversation, and/or the like.

In some embodiments, the determined response times used to select thesubset of smart radios are added to experience profiles of workersassociated with the smart radios. For example, an average response timethat a worker takes to read or interact with a communication via a smartradio is stored in an experience profile for the worker.

As such, in some embodiments, selection of smart radios is further basedon experience profiles of the workers associated with the smart radios.For example, workers with an average response time less than a thresholdare automatically selected for the first responder subset. Use ofresponse time metrics in worker experience profiles conserves some timethat would be spent detecting response activities on the smart radiosand determining (and ordering) response times.

Smart Radio Location Displays

Embodiments described herein relate to temporally-dynamic visualizationof smart radio locations within a worksite. According to exampleembodiments, a user interface is configured to display a slice orsnapshot of smart radio locations, with multiple different slices orsnapshots being available for display. Thus, embodiments fortemporally-dynamic visualization of smart radio locations enable a userto easily view different locations and arrangements of smart radios overtime.

In some embodiments, the user interface is provided via a smart radio(e.g., via a display screen 130 of a smart radio as illustrated anddescribed in relation with FIG. 1 ). In some embodiments, the userinterface is provided via a computer system as in the example computersystem 2300 illustrated and described in more detail with reference toFIG. 23 .

FIG. 13 illustrates an example system in which a temporally-dynamicvisualization of smart radio locations is provided. The example systemincludes a user interface 1300 in which smart radio locations aredisplayed and a location database 1302 stores sets of location data thateach correspond to a point or slice in time. With the user interface1300 and the location database 1302, the example system is configured toovercome technical challenges that arise with large volumes of smartradio locations over long periods of time. According to exampleembodiments, selection of a time slice via the user interface 1300allows efficient access and retrieval of a corresponding set of locationinformation from multiple sets stored in the location database 1302.

Referring first to the user interface 1300, the example user interface1300 includes a map portion 1304 that is configured to indicatelocations of multiple smart radios. For example, in the map portion1304, a map layer displays a representation of a worksite or facilitywithin which smart radio locations are indicated.

In some embodiments, the map layer additionally displays geographicareas within the worksite or facility. In some embodiments, thegeographic areas within the worksite or facility are geofenced, and theexample user interface 1300 is configured to enable selection ofgeofenced areas displayed in the map portion 1304 for area-specificoperations. For example, a user selects a geofenced area displayed inthe map portion 1304 to cause an area-wide communication to betransmitted, to establish response-controlled communications within thegeofenced area (e.g., via the example process of FIG. 12 ), and/or thelike.

In the map portion 1304, the smart radio locations within therepresentation of the worksite are indicated via an overlay layer thatis displayed over the map layer. The overlay layer includes indicators1306 that represent locations for multiple smart radios within theworksite or facility. In some examples, the indicators 1306 in theoverlay layer are configured to indicate additional information, such asa name or identifier for a worker assigned to a smart radio, an image ofthe logged-in user, a battery level of the smart radio, and/or the like.

In particular, the indicators 1306 of the overlay layer represent smartradio locations for a given point in time, and the indicators 1306 aregenerated based on location information that corresponds to a givenpoint in time. The location information used to generate the indicatorsof smart radio locations is accessed and retrieved from the locationdatabase 1302, which stores multiple sets of location information, eachcorresponding to different points or slices in time.

Thus, according to example embodiments, display of smart radio locationsfor a given point in time is based on precise and efficient selection ofrelevant location information from sets of location information storedin the location database 1302. In some embodiments, the locationdatabase 1302 stores an index that describes relationships between setsof location information and different points in time.

In some embodiments, the location database 1302 stores sets of locationinformation that each include a corresponding time point identifier(e.g., a timestamp). In some embodiments, each set of locationinformation includes locations (e.g., coordinate values) for each of aplurality of smart radios at a corresponding point in time. For example,the location database 1302 stores a plurality of data objects, with eachdata object including a time point identifier and a set of locationinformation. In some embodiments, a data object stores a previouslygenerated overlay layer for a corresponding point in time.

As such, the location database 1302 is configured to store locationinformation in a time-wise organization to enable efficient access andretrieval of relevant portions of location information. FIG. 13illustrates the location database 1302 separately storing a first dataobject for a first time slice (e.g., Time Slice A), a second data objectfor a second time slice (e.g., Time Slice B), and so on.

Thus, given an indication of a specific point in time, a relevant dataobject (e.g., relevant portions of location information) stored in thelocation database 1302 is identified from a plurality of data objects.In some embodiments, the indication of the specific point in time isbased on a user selection that is made through the user interface 1300.In some embodiments, the user interface 1300 includes a time selectionportion 1308. For example, in FIG. 13 , the time selection portion 1308includes a slider interface via which the user selects a specific pointin time.

In response to a user interaction with the time selection portion 1308of the user interface 1300, a query or request is sent to the locationdatabase 1302 to cause the retrieval of the relevant data object. Usingthe time selection portion 1308 and the location database 1302 storingseparate data objects for different time points/slices, a user is ableto cause any given snapshot of smart radio locations to benon-chronologically or non-sequentially displayed.

A display of different snapshots of smart radio locations is shown inFIG. 13 shown by dotted paths. In some examples, dynamic userinteraction with the time selection portion 1308 causes the appearanceof the indicators 1306 traveling along the dotted paths. While the timeselection portion 1308 is exemplified as a slider interface in FIG. 13 ,it will be appreciated that other mechanisms are implemented in otherexamples. In some embodiments, non-chronological or non-sequentialselection of time points or slices is enabled.

According to the described embodiments, temporally-dynamic visualizationof smart radio locations is based on user interaction with a userinterface and selection of a relevant data object from a plurality oftime-specific data objects stored in a database. In an exampleoperation, a first overlay layer that includes indicators of smart radiolocations for a first point in time is displayed in a user interface(e.g., user interface 1300). Responsive to a user interaction with atime selection portion (e.g., portion 1308) of the user interface thatselects a second point in time, a query that indicates the second pointin time is sent to the database (e.g., location database 1302). Thedatabase stores a plurality of data objects that each include a set oflocation information for a corresponding point in time. In someembodiments, the set of location information included in a data objectincludes a previously generated overlay layer. Based on the indicationof the second point in time, a data object is then selected and used togenerate a second overlay layer. The second overlay layer includesindicators of smart radio locations for the second point in time and isdisplayed on the user interface.

Equipment Location Monitoring

Embodiments described herein relate to mobile equipment tracking viasmart radios as triangulation references. In this context, mobileequipment refers to work site or facility industrial equipment (e.g.,heavy machinery, precision tools, construction vehicles). According toexample embodiments, a location of a mobile equipment is continuouslymonitored based on repeated triangulation from multiple smart radioslocated near the mobile equipment. Improvements to the operation andusage of the mobile equipment are made based on analyzing the locationsof the mobile equipment throughout a facility or worksite. Locations ofthe mobile equipment are reported to owners of the mobile equipment, orentities that own, operate, and/or maintain the mobile equipment. Mobileequipment whose location is tracked include vehicles, tools used andshared by workers in different facility locations, tool kits andtoolboxes, manufactured and/or packaged products, and/or the like.Generally, mobile equipment is movable between different locationswithin the facility or worksite at different points in time.

In some embodiments, a tag device is physically attached to a mobileequipment so that the location of the mobile equipment is monitored. Acomputer system (e.g., example computer system 2300, cloud computingsystem 220, a smart radio, an administrator smart radio) receives tagdetection data from at least three smart radios based on the smartradios communicating with the tag device. Each instance of tag detectiondata received from a smart radio includes a distance to the tag deviceand a location of the smart radio.

In some embodiments, the tag detection data is received from smartradios owned or associated with different entities. That is, differentsmart radios that are not necessarily associated with the same givenentity (e.g., a company with which various operators at the worksite areemployed) as a given mobile equipment are used to track the given mobileequipment. As such, ubiquity of smart radios that are capable or allowedto track a given mobile equipment (via the tag device) is increasedregardless of ownership or association with particular entities.

In some embodiments, the tag device is an AirTag™ device. In someembodiments, the tag device is associated with a detection range. Thetag device is detectable via wireless communication by other devices,including smart radios, located within the detection range of the tagdevice. For example, a smart radio detects the tag device via Wi-Fi,Bluetooth, Bluetooth Low Energy, near-field communications, cellularcommunications, and/or the like. In some embodiments, a smart radio thatis located within the detection range of the tag device detects the tagdevice, determines a distance between the smart radio and the tagdevice, and provides the tag detection data to the computer system.

From the tag detection data, the computer system determines a locationof the tag device, which is representative of the location of the mobileequipment. In particular, the location of the mobile equipment istriangulated from the known locations of multiple smart radios and therespective distances to the tag device, using the tag detection data.

Thus, the computer system determines the location of the mobileequipment and is configured to continuously monitor the location of themobile equipment as additional tag detection data is obtained over time.

In some embodiments, the determined location of the mobile equipment isindicated to the entity with which the mobile equipment is associated(e.g., an owner, a user of the mobile equipment, etc.). As discussed, insome examples, the location of the mobile equipment is determined basedon triangulation of the tag device by different smart radios owned bydifferent entities. If a mobile equipment location is determined viamultiple entities, the mobile equipment location is only reported to therelevant entity, such that mobile equipment locations are not insecurelyshared across entities.

In some embodiments, mobile equipment location is determined and trackedaccording to privacy layers or groups that are defined. For example, atag for a mobile equipment is detected and tracked by a first group ofentities (or smart radios assigned to a first privacy layer), and thedetermined location is reported to a smaller group of entities (ordevices assigned to a second privacy layer).

Various monitoring operations are performed based on the locations ofthe mobile equipment that are determined over time. In some embodiments,a usage level for the mobile equipment is automatically classified basedon different locations of the mobile equipment over time. For example, amobile equipment having frequent changes in location within a window oftime (e.g., different locations that are at least a threshold distanceaway from each other) is classified at a high usage level compared to amobile equipment that remains in approximately the same location for thewindow of time. In some embodiments, certain mobile equipment classifiedwith high usage levels are indicated and identified to maintenanceworkers such that usage-related failures or faults can be preemptivelyidentified.

In some embodiments, a resting or storage location for the mobileequipment is determined based on the monitoring of the mobile equipmentlocation. For example, an average spatial location is determined fromthe locations of the mobile equipment over time. A storage locationbased on the average spatial location is then indicated in arecommendation provided or displayed to an administrator or other entitythat manages the facility or worksite.

In some embodiments, locations of multiple mobile equipment aremonitored so that a particular mobile equipment is recommended for useto a worker during certain events or scenarios. For example, in amedical emergency situation, a particular vehicle is recommended andindicated to a nearby worker based on a monitored location for theparticular vehicle being located nearest to the worker. As anotherexample, for a worker assigned with a maintenance task at a locationwithin a facility, one or more maintenance tool kits shared amongworkers and located near the location are recommended to the worker foruse.

Accordingly, embodiments described herein provide local detection andmonitoring of mobile equipment locations. Facility operation efficiencyis improved based on the monitoring of mobile equipment locations andanalysis of different mobile equipment locations.

Area-Based Productivity Tracking

According to example embodiments, smart radios are assigned to differentworkers who are associated with different roles. For example, a firstsmart radio is assigned to and used by an administrator, a second smartradio is assigned to and used by a medic, and a third smart radio isassigned to and used by a maintenance technician.

The different roles associated with different workers are representativeof different operations and tasks performed by the workers, which aremore relevant to certain areas within a facility than other areas. Assuch, in some embodiments, certain geofenced areas of a facility areidentified as activity areas for a given role, and different roles havedifferent activity areas. For example, a break or rest area is anactivity area for a medic but is not an activity area for a technician.As another example, a base or office area is an activity area for anadministrator but is not an activity area for a vehicle operator.

That is, in some embodiments, activity areas are identified for a workerrole based on an expectation that the tasks associated with the workerrole are productively performed within the activity areas. Thus, aworker is expected to have an increased productivity while locatedwithin the activity area than while located outside of the activityarea.

Embodiments described herein use role-specific activity areas andgeofencing to classify activity levels for workers. FIG. 14 provides aflow diagram that illustrates an example process for classifying workeractivity based on smart radio locations with role-specific activityareas. In some embodiments, the illustrated process is performed by acloud computing system 220 (e.g., shown in FIG. 2A). In someembodiments, the illustrated process is performed by a computer system,for example, the example computer system 2300 illustrated and describedin more detail with reference to FIG. 23 . Particular entities, forexample, the smart radios (e.g., smart radios 1105, smart radios 224),perform some or all of the steps of the process in some embodiments.Likewise, some embodiments include different and/or additional steps, orperform the steps in different orders.

In step 1402, a plurality of activity areas relevant to a smart radioare identified. The activity areas are geofenced areas that are mappedto a worker role of a worker who is currently using the smart radioand/or assigned to the smart radio. In some examples, metadata generatedwith a definition of a geofence includes an indication of worker rolesfor which the geofence is an activity area.

In step 1404, activity measurement data is generated. In someembodiments, the activity measurement data describes an activity orproductivity level of a worker, or an estimation of whether the workeris actively performing assigned tasks.

For example, the activity measurement data includes a first activitylevel determined for the worker based on the smart radio (and theworker) being located within an activity area for the worker's role. Thefirst activity level is indicative of increased productivity of theworker due to the worker being located within an activity area where theassigned tasks are intended to be performed.

In some examples, the activity measurement data includes a secondactivity level for the worker that is determined based on micromovementsof the smart radio. For example, a relatively high degree ofmicromovements of the smart radio is indicative of the worker activelyperforming a physical task, while a relatively low degree ofmicromovements of the smart radio suggests that the worker is static.Thus, further to the worker being located within an activity area,physical activity of the worker is estimated and used to classify afurther activity or productivity level of the worker.

In some embodiments, micromovements refer to small-scale changes inlocation of the smart radio, or movements that do not exceed a thresholddistance within a certain time. For example, some example micromovementsare detected and measured via a position tracking component of a smartradio (e.g., position tracking component 125 in FIG. 1 ). In someembodiments, micromovements include changes in three-dimensionalposition of the smart radio, for example, changes detected by agyroscope, accelerometer, and/or similar sensors in the smart radio.Generally, from data collected at the smart radio, a degree ofmicromovement of the smart radio is determined and used to classify asecond activity level for the worker.

In some embodiments, the activity measurement data is time-dependent andincludes times at which a first activity level is classified for theworker, times at which a second activity level is classified for theworker, and/or the like.

In step 1406, management operations of the worker are performed based onthe activity measurement data. In some embodiments, clock-ins of theworker are captured based on the activity measurement data including afirst activity level or a second activity level for the worker. In someembodiments, time data that includes lengths of time that the workerspends at the first activity level and/or the second activity level isdetermined from the activity measurement data. In some embodiments, thetime data is automatically provided to HR software and systems, suchthat manual input of the time records by the worker is not needed. Insome embodiments, the time data is stored with profiles associated withthe worker, such as an experience profile.

In some embodiments, the activity measurement data is used to monitorexposure of the worker to hazardous conditions. For example, from theactivity measurement data, a length of time that the worker isphysically active in certain conditions (e.g., excessive sunlight, anoxygen-depleted environment, a room with a cold temperature) ismonitored and compared against safety thresholds. Thus, in someexamples, worker activity is measured and used to improve worker safety.

In some embodiments, an automated alert is transmitted to a given workerthat has spent less than a threshold length of time in an activity areaor has spent longer than a threshold length of time outside of anactivity area. For example, a length of time that a worker is notclassified at either a first activity level or a second activity levelis monitored and compared against a threshold to determine whether totransmit an alert to the smart radio for the worker.

In some embodiments, the management operations includes generating aworker activity user interface for display. FIG. 15 illustrates anexample worker activity user interface 1500.

In some embodiments, the worker activity user interface 1500 is providedfor display at an example computer system 2300, and in particular, at avideo display 1218 thereof. In some embodiments, the example computersystem 2300 is an administrator system, and the worker activity userinterface 1500 is provided for display to an administrator. In someembodiments, the example computer system 2300 is a smart radio, and theworker activity user interface 1500 is provided for display via adisplay screen 130 of the smart radio.

As illustrated in FIG. 15 , the worker activity user interface 1500 isconfigured to indicate the activity measurement data. In someembodiments, the worker activity user interface 1500 includes a graph ofpercentage of time in an activity area. For example, a data pointassociated with a given worker is located on the graph to represent apercentage of total time that the given worker is located within anactivity area for the given worker's role. In FIG. 15 , multiple datapoints are located on the graph and shown as circles of varying sizes.The respective size of a circle indicates a number of data points thatoverlap.

That is, in some embodiments, the worker activity user interface 1500indicates a length of time that each worker is classified with a firstactivity level. In some embodiments, the worker activity user interface1500 additionally or alternatively indicates a length of time that eachworker is classified with a second activity level, or is exhibitingthreshold physical micromovements within an activity area.

In some embodiments, as illustrated in FIG. 15 , worker-specificactivity measurement data is aggregated based on groupings of workers.Accordingly, in some embodiments, an average length of time that a groupof workers are classified with a first activity level and/or classifiedwith a second activity level is indicated in the worker activity userinterface 1500. For example, workers are grouped by affiliation withcertain entities (e.g., by company), by worker roles (e.g., crafts),and/or the like.

It will be appreciated that the worker activity user interface 1500includes other indications of the activity measurement data, in someexamples. For example, a ranked list or leaderboard of workers (orgroups thereof) that is sorted by lengths of time at a first activitylevel is displayed via the worker activity user interface 1500.

Automated Geofencing

As discussed herein, geofences are used to define real-world geographicareas. In various examples, the geographic areas that take shape in thereal-world are difficult for geofences to accurately represent. In someexamples, a geographic area is highly polygonal, elliptical, orsimilarly complex.

Meanwhile, to minimize data footprint, a geofence is defined as a circlewith a center and a radius. While a geofence is efficiently defined withthe two data points, inaccuracies in representing a geographic area thatis not perfectly circular arise. Such inaccuracies result inmisclassifications of a smart radio being located in a certaingeographic area when the smart radio is actually outside of the area.

FIG. 16A illustrates an example scenario in which inaccuracy of geofencerepresentation of an area 1600 leads to misclassification of smartradios. As illustrated, area 1600 is polygonal, while geofence 1602 iscircular. As a result, use of the geofence 1602 to represent the area1600 results in a misclassification of a particular smart radio 1605 asbeing located in the area 1600 despite being located outside of the area1600.

Embodiments described herein improve accuracy of geofence representationof real-world geographic areas based on defining a geographic area usinga plurality of circular geofences. FIG. 16B illustrates a plurality ofcircular geofences 1602 used to define a border of the area 1600. Theplurality of circular geofences 1602 form an aggregate geofence, withwhich smart radio locations are classified. For example, if a smartradio location is completely surrounded by the plurality of circulargeofences 1602, the smart radio location is determined to be within theaggregate geofence and is classified as being located inside of the area1600. Improvements to the accuracy of location-specific andlocation-based operations for smart radios/apparatuses are thereforeprovided.

“Blind Use” Interface

The smart radio is designed and configured to be used “blind.”References to the word “blind” refer to the positional use of the smartradio. FIG. 17 is an illustration of “blind” operation of a smart radio.The smart radio is intended to be worn on the chest or shoulder region1700 (e.g., via a Klick Fast™ bracket) while the screen faces away fromthe user/wearer. The use of the term “blind” may not be constrained toonly refer to physical blindness, but rather include examples in whichthe device is operable while the user is not looking at the device orthat the device is only in peripheral vision.

In some embodiments, the smart radio includes switches, buttons,sensors, and/or other features that detect whether the smart radio isbeing worn or attached to a user in a blind use position. For example,as described below in connection with FIG. 18 , the smart radio includesa bracket via which the smart radio is attached to a user (e.g., at afront torso strap, mount, or the like worn by the user as shown in FIG.17 ), and the smart radio includes a switch, button, sensor, and/or thelike to detect whether the bracket is presently engaged in attachment tothe user. In some embodiments, the smart radio uses an on-boardaccelerometer to determine whether the smart radio is currently in useand attached to a user. For example, based on a threshold degree ofmovement being measured by the on-board accelerometer, the smart radiodetermines that the smart radio is in a blind use position.

A user who is working and has the smart radio attached to themselves isnot going to want to remove the smart radio frequently to focus on theradio. In order to operate blind, the smart radio includes large, raisedbuttons that are easy to find by touch, even through heavy work gloves .Although there is a screen that faces the opposite direction, navigationthrough said display interface must be simple. Additional featuresinclude use of vibration and channel recognition sounds that alert whenchanging to a given channel. Another important factor is the limitednumber of buttons. Referring again to FIG. 5 , on the front face, thesmart radio has only four buttons that perform navigation. The up anddown buttons 508, 512 make use of a concave surface that enables agloved hand to find ridges defining the beginning of the back/homebutton 504 and the selection button 516. The concave design furtherenables gloved identification of up and down 508, 512.

A front facing RGB LED light that is sufficiently bright to reflect offambient surfaces indicates messages or communications in channelsassociated with particular colors. For example, audio/text on anemergency channel causes the LED to present as red. Audio/text from anadministrator causes the LED to present as purple, and audio/text on adifferent channel causes the light to present as a different colorspecifically associated with the channel. In some embodiments, thedisplay screen colors each channel the same color that the LED changesto based on the incoming audio/text. In some embodiments, each channelbeing presented by the smart radio (e.g., according to a logged-in user,according to a geofence that the smart radio is located in) isassociated with a unique color. In some embodiments, the smart radioincludes lensing in connection with the LED that facilitates thescattering of light emitted by the LED. In some embodiments, the LEDlight is located on the front face of the smart radio. In someembodiments, the smart radio includes the LED light or a second LEDlight on the top face such that a user to which the smart radio isattached can look down and see the emitted light. In some embodiments,the LED light is only operated based on the smart radio determining thatthe smart radio is in the blind use position, thereby conserving powerwhen not in the blind use position.

The smart radio is further configured with a rear-facing speaker. FIG.18 is a cross-sectional diagram of a smart radio 1800 illustratingspeaker placement. The speaker 1802 is positioned within the smart radio1800 toward the rear of the assembly, behind a mounting bracket 1804 anda rear outer housing 1806. The mounting bracket 1804 enables the smartradio to incorporate a stud that mounts to external surfaces (e.g., aKlick Fast™ stud). Configuring the speaker 1802 to be rear facingprovides several advantages.

As a first advantage, the speaker 1802 is positioned closer to theexterior of the device 1806 (e.g., the speaker is not obstructed by adisplay screen that must be on the front). The proximity of the speaker1802 to the speaker hole 1808 on the exterior of the device increasesoutput volume as there is less sound lost internally to the device 1800.Second, the speaker hole 1808 is not limited by the size of the displayscreen 1810 and is enabled to be larger; therefore, the device 1800 isenabled to emit more sound. Additionally, the larger speaker hole 1808enables less distortion (muffling) of the sound output. Third, thedecrease in distance between the speaker 1802 and the speaker hole 1808enables the use of a speaker mesh that provides the smart radio somemeasure of water resistance. Finally, the rear facing speaker 1802increases the volume when in “blind use.”

Aiming the speaker toward a mounting bracket 1804 (e.g., such as a KlickFast™ bracket) positioned on a user's body provides directional routingof sound off the user's body and toward the head. Thus, in the contextof a body mounted bracket 1804, the rear-facing speaker 1802 increasesthe perceived volume per output power. Increased volume is a relevantconcern where a user is operating loud machinery or wearing headphones.

Another element that enables blind operation is the use of smart radiovibrations and channel identification sounds. As the user scrollsthrough channels (e.g., using the large forward-facing buttons), thesmart radio emits an auditory notification indicating the channel theuser is on. Embodiments of the auditory notification include spokenrecitations of the channel title and/or chimes that are associated withspecific channels (e.g., a first note(s) of a siren sounds in responseto being switched to an emergency channel). In some embodiments, theauditory renderings of text-based messages received by the smart radioare also played via the rear-facing speaker. For example, the smartradio automatically renders a playback of text-based messages receivedwhile in the blind use position. In some embodiments, the smart radioreceives a text-based message, and in response to a user input (e.g.,via the large front buttons, via the PTT button), the smart radiorenders an auditory rendering of the text-based message.

In some embodiments, the smart radio is configured to enhancerecognition and interpretation of received messages, including live PTTor streamed messages and text-based messages (and auditory renderingsthereof). According to example embodiments, the smart radio isconfigured to translate a received message from a first language to asecond language. For example, a text-based message is defined inSpanish, and the smart radio translates the text-based message toEnglish (e.g., for the user to read in a communication thread), orgenerates an English auditory rendering of the Spanish message. Thetarget language of the translation can be associated with the user ofthe smart radio. For example, profile data associated with the user canindicate that the user is fluent in one or more particular languages,and the smart radio translates received messages in a different languageto the user's fluent languages. In some embodiments, the smart radiolocally stores language models, automatic speech recognition (ASR)models, translation models, dictionary mappings, and/or the like thatthe smart radio uses to generate a translated auditory rendering. Insome embodiments, the smart radio interfaces with a translation service,for example via an API, to obtain a translated message that the smartradio can then dictate or auditorily render.

A smart radio's improvement to message interpretation/recognitionincludes including auditory identifications of the sender of a receivedmessage (e.g., a live PTT or streamed message, a text-based message)and/or the communication channel/thread of the received message. Whenreceiving an auditory rendering of a message, a user might not be ableto recognize a voice of a sender of the message (or the voice is acomputer-generated voice that dictates a text message) and may beuninformed as to the sender of the message. Accordingly, the smart radioincludes an identification of the sender of a message when playing anaudio rendering of the message. In some embodiments, the smart radioalternatively or additionally includes an identification of acommunication channel or thread in which the message is received withthe audio rendering of the message. For example, the smart radioindicates that the message was received in an emergency channel, anoperating channel, a particular radio frequency channel, and/or thelike.

In some embodiments, the smart radio determines whether to append thesender/channel identification to the audio rendering of the message orto precede the audio rendering of the message with the sender/channelidentification. In some embodiments, the smart radio appends thesender/channel identification based on the message being a live PTTmessage or a streamed message. In doing so, the message contents arecommunicated to its recipient (the user of the smart radio) first,enabling the recipient to act on the live PTT or streamed message withmore priority or urgency. In some embodiments, the smart radio precedesthe message (or auditory rendering thereof) with the sender/channelidentification. Compared to live PTT or streamed messages, text-basedmessages and auditory renderings thereof are communicated with lessurgency, thus permitting preceding sender/channel identification andresultant delay of message communication.

The navigation menus themselves include limited options that cyclethrough radio channels. The channels update based on the job, employer,facility, or geofence where the user is working at a given time. Theuser is assigned to a given set of tasks/employers/facilities based onthe operation of an administrative user. The geofence within which auser is physically present determines geofence based channels. A sampleset of channels that are automatically populated includes: emergencychannel, dispatch channel, daily team members channel, geofence regionchannel, and channels associated with individuals that are part of anyof the mentioned groups.

Single Threaded Social Features

An important feature of a smart radio is streaming audio that operateson a push-to-talk (PTT) basis. However, modern social features, such asthose in smart phones don't operate with the simplicity of a radio.Embodiments disclosed herein include a new interface that combinesmodern social features such as text threads, including SMS and MMS typemessaging, along with PTT radio improvements on existing interfaces.

A known interface includes a scrolling screen that includes a list ofopen and/or available text threads. When a user clicks/touches a textthread, the thread opens, and the available features of that thread arepresented to the user. When a user receives a message, the devicetypically presents a notification that enables a quick link to therelevant social thread.

Disclosed herein are interfaces that integrate PTT features with socialtext threads. In some embodiments, when a smart radio begins receivingstreaming audio from an external device, the source of the audio isidentified via audio metadata. The audio metadata includes suchembodiments as packet headers that identify a sender or a sender's role.In some embodiments, the audio metadata is represented by a channel fromwhich the audio is received or non-audio data that is transmitted withthe audio data (e.g., as a portion of the payload).

As described elsewhere in this application, the PPT features of thesmart radio operate using at least one of the on-board antennae. Thenetworks used are any of a plurality of local private cellular networks,external public or commercial cellular networks, Wi-Fi (IEEE 802.11networks), or hopping two-way radio protocol (as described elsewhereherein).

When received, the audio data is played back immediately, and the smartradio automatically shifts display from a current screen to atext/social thread associated with the sender. In some embodiments, thesmart radio screen remains off (to save power), but when accessed, itautomatically displays the text/social thread associated with thesender. In some embodiments, the streaming audio that is received isautomatically transcribed and presented in the social thread. In doingso, the user is able to later reference the received audio message, forexample, when the smart radio is no longer in the blind use position.

Further or subsequent PTT streaming audio transmissions are presented inthe same thread where SMS and MMS style messages are received. Forexample, the user responds to the received audio message by activatingthe PTT button (e.g., by depressing the PTT button for a continuouslength of time) and uttering a response. The response is streamed backto the origin device of the received audio message and/or the responseis automatically transcribed and added to the text thread. In someembodiments, the uttered response is both streamed as audio andtranscribed as text for the recipient (the origin device of the audiomessage received by the smart radio). In some embodiments, the utteredresponse is either streamed as audio or transcribed as text depending onthe recipient, for example, whether the recipient is also a smart radioor a smart radio in a blind use position. In some embodiments, uponreceipt of a streaming audio message, the smart radio provides a periodfor quick response within the same social thread (e.g., 15 seconds).After the period elapses without action, the smart radio returns fromthe relevant social thread to a home screen. Similarly, when the userinterface is in a text thread associated with a given user or group ofusers, activating a PTT key initiates streaming audio with that user orgroup of users.

The disclosed interface is applicable to different hardware platforms aswell. The smart radios operate on a network that is configured toinclude additional devices such as mobile phones. On mobile phones, thedisclosed messaging interface is included in a mobile application. Themobile application is configurable to supplement, augment, or replace adefault messaging application of the mobile phone.

In some embodiments of the disclosed communication interface, where auser is actively streaming audio (e.g., via PTT) on one channel andstreaming audio is received on another channel, the audio playback waitsuntil the user is done speaking to shift the display to the incomingmessage thread, and playback of the incoming streaming audio.

Roaming Channels

The smart radio is further configured to roam channels based on presencewithin a geofence. FIG. 19 is a flowchart illustrating automatic roamingof channels. As described above, an administrative user assigns users toparticular teams, jobs, or facilities and the user's smart radiochannels are determined therefrom. However, in some embodiments, agreater number of channels are derived from the geofence that the user(e.g., and the smart radio they are logged into) is present in. In step1902, when a user logs into a smart radio using the global directory/tap& go, the user is present within a given geofence. In step 1904, thegeofence the user is present in triggers provisioning of their smartradio to the employer, job, and teams of most associated with thatgeofence.

For example, although an administrative user is able to manually assignusers to associated or assigned groups, a user using their ownpreconfigured geofence allows for less steps required for managingindividual users that may be largely transient. Where users login, afirst geofence provisions their device with some channels (e.g.,associated the user with the employer for the day). In step 1906, theuser is then instructed to go to a second location where a secondgeofence further provisions the smart radio for the day (e.g.,associating the user with a given facility/job for the day).

In step 1908, where the user is subsequently directed to a thirdlocation, a third geofence revises the prior provisioning of the smartradio associated with the user's profile. Revisions to the user'scurrent operation modify the radio channels available to the user on thesmart radio. The changes to the available channels are an automatic andseamless process for the user.

Long Press PTT Interface

FIGS. 20A and 20B illustrate a message thread user interface 2000implementing long presses as a push-to-talk feature. Some devices, suchas mobile phones, operate on a similar network as the smart radios andconnect via a mobile application. The mobile application replaces orsupplements the existing messaging application on the mobile phone.Mobile phones typically have additional interface options, such as touchscreens and do not have features such as PTT radio toggles. Using themobile application described herein, a display screen includes a numberof threads 2002 associated with individuals or groups for SMS, MMS, andstreaming audio messaging. Each thread 2002 includes an avatar 2004, asummary of most recent text 2006, a playback button 2008 that plays themost recent streaming audio received and/or a text-to-speech auditoryoutput. Devices that incorporate a touch screen interface further enablea long press 2010 (indicated by a highlighted display) on a givenmessaging thread 2002 to enable PTT features with the individual orindividuals associated with the selected messaging thread 2002.

Existing interfaces use long press interaction to open an additionalmenu that enables archival, deletion, pinning, or muting of themessaging thread. Use of long press interaction on a given messagingthread to enable a PTT feature to the members of the messaging threadenables quick cycling through different streaming audio conversations.

PTT on Motion-Connected Lock Screen

Embodiments of the smart radio include motion-based locking. While thedevice is worn, the on-board accelerometer detects small movements ofthe device that keep the device unlocked for use by the user wearing thedevice. When the device is set down, the device locks. Locking a smartradio is different than locking a mobile phone. The smart radio's PTTfunctionality still operates and the radio still emits streaming audiowhen received. However, locked devices will not enable an operator toview text message history or transcriptions of the streaming audio. Alock screen requests reauthorization (e.g., through a pin code or “tap &go” of work badge). The PTT features remain functional on all channelsbut text communication becomes limited. Text communication is limited ascompared to PTT because a given user's voice is recognizable, but theirtext speech may not be.

Additionally, in a given use case, user identification is easilydisambiguated by administrator analytics. If a given user is carryingtheir own smart radio, there is an administrator record of that smartradio approaching the stationary radio followed by radio operation onthe otherwise motionless radio. It is easy for a computer to attributethe message to the user who was observed approaching the stationaryradio. Conversely, once a user's private messages have been revealed toother users, clarification of a use record cannot fix the situation.Thus, PTT use of the radio on all channels is not prevented on the lockscreen, but review of past recorded messages is prevented.

Generally, smart phones lock when placed in a user's pocket (e.g.,worn); however, use of the smart radio is often from a worn position andthus detection and characterization of worn movement keeps the deviceunlocked. Conversely, the smart radio seeks security when it is leftsomewhere.

Cable Attenuation Power Management

The smart radio is configured to operate as a mounted sensor (e.g.,camera, wireless communicator, etc.). Mobile devices such as the smartradio typically charge using a specified voltage or voltage range (e.g.,5-9.4V). The charging voltage presents a problem for providing wiredsources of power to the mounted device because the device must be closeto the source of power. This is because voltage attenuation occurs overlong cables, and the voltage at the device is no longer what the deviceexpects. Where a device receives a voltage it does not expect, it willnot charge. Power sources are frequently not particularly close to everylocation where one may desire a mounted sensor. Typically, a devicereceives 9.4V and a transformer modifies that voltage to 5V. Valuesunder 5V become problematic for the device.

Accordingly, some embodiments of the smart radio are configured tocharge using a variable peak or root mean square (RMS) voltage. Thesmart radio thus operates on a first power setting when in radio modeand another power setting when in mounted sensor mode (e.g., a tricklecharging mode). When operated in a mounted sensor mode, the device usesless power because the device need not operate all devices on board(e.g., the display screen, the accelerometer, etc.). The power operationmodes are set via onboard software and control charging voltage and/oroperable on-board equipment.

FIG. 21 is a flowchart illustrating power mode selection. Control of thepower modes is operable by remote control signals, local applicationsettings, or by physical switch. An embodiment of a physical switch is abutton under an external shell of the smart radio that is depressedbased on the insertion of a screw connected to a power cable plug. Thescrew locks the power cable in place on the smart radio and furtherdepresses the physical switch. Numerous embodiments of threaded powercables known in the art function as mode triggers using the physicalswitch on the smart radio.

In step 2102, the smart radio is attached to power (of an unknownvoltage and configuration). In step 2104, the device receivesconfiguration via control signal, application settings, or physicalswitch and modifies the power consumption mode. Alternatively, in step2106, the device detects an available voltage (either of RMS voltage orpeak voltage). In step 2108 and in response to detection of the voltagebeing above or below a predetermined threshold, or within any ofmultiple expected ranges, the device modifies its power consumption modeto a corresponding mode. In some embodiments, the lower voltage mode,operates at less than 5V and the higher voltage mode operates at 5V orhigher.

Embodiments of the smart radio as described herein make use of longerpower cables such as those extending to 200 feet, 300 feet, 400 feet, orsmaller lengths therebetween. Given the range of multiple hundreds offeet, the mounted sensor embodiment of the smart radio is enabled toposition within significant distances from dedicated power sources. Thevoltage attenuation over multiple hundred feet of cable is too great foroff the shelf mobile phones to function. Similarly, mobile phones haveno reason to operate at such low voltages (e.g., 3-4V, or3.9V)—specifically, smart phones are designed to operate all on boardsensors as frequently as possible. Thus, including multiple power modesthat operate with fewer sensors on smart phone devices iscounter-intuitive.

Accordingly, embodiments of the smart radio are modified to operate at amobile device voltage, and at a mounted sensor voltage that accounts forattenuation of over 100 feet of power cable.

Computer Embodiment

FIG. 22 is a block diagram illustrating an example ML system 2200, inaccordance with one or more embodiments. The ML system 2200 isimplemented using components of the example computer system 2300illustrated and described in more detail with reference to FIG. 23 . Forexample, portions of the ML system 2200 are implemented on the apparatus100 illustrated and described in more detail with reference to FIG. 1 ,or on the cloud computing system 220 illustrated and described in moredetail with reference to FIG. 2A. Likewise, different embodiments of theML system 2200 include different and/or additional components and areconnected in different ways. The ML system 2200 is sometimes referred toas a ML module.

The ML system 2200 includes a feature extraction module 2208 implementedusing components of the example computer system 2300 illustrated anddescribed in more detail with reference to FIG. 23 . In someembodiments, the feature extraction module 2208 extracts a featurevector 2212 from input data 2204. For example, the input data 2204includes location parameters measured by devices implemented inaccordance with embodiments disclosed herein. The feature vector 2212includes features 2212 a, 2212 b, . . . , 2212 n. The feature extractionmodule 2208 reduces the redundancy in the input data 2204, for example,repetitive data values, to transform the input data 2204 into thereduced set of features 2212, for example, features 2212 a, 2212 b, . .. , 2212 n. The feature vector 2212 contains the relevant informationfrom the input data 2204, such that events or data value thresholds ofinterest are identified by the ML model 2216 by using a reducedrepresentation. In some example embodiments, the followingdimensionality reduction techniques are used by the feature extractionmodule 2208: independent component analysis, Isomap, kernel principalcomponent analysis (PCA), latent semantic analysis, partial leastsquares, PCA, multifactor dimensionality reduction, nonlineardimensionality reduction, multilinear PCA, multilinear subspacelearning, semidefinite embedding, autoencoder, and deep featuresynthesis.

In alternate embodiments, the ML model 2216 performs deep learning (alsoknown as deep structured learning or hierarchical learning) directly onthe input data 2204 to learn data representations, as opposed to usingtask-specific algorithms. In deep learning, no explicit featureextraction is performed; the features 2212 are implicitly extracted bythe ML system 2200. For example, the ML model 2216 uses a cascade ofmultiple layers of nonlinear processing units for implicit featureextraction and transformation. Each successive layer uses the outputfrom the previous layer as input. The ML model 2216 thus learns insupervised (e.g., classification) and/or unsupervised (e.g., patternanalysis) modes. The ML model 2216 learns multiple levels ofrepresentations that correspond to different levels of abstraction,wherein the different levels form a hierarchy of concepts. The multiplelevels of representation configure the ML model 2216 to differentiatefeatures of interest from background features.

In alternative example embodiments, the ML model 2216, for example, inthe form of a CNN generates the output 2224, without the need forfeature extraction, directly from the input data 2204. The output 2224is provided to the computer device 2228, the cloud computing system 220,or the apparatus 100. The computer device 2228 is a server, computer,tablet, smartphone, smart speaker (e.g., the speaker 632 of FIG. 6 ),etc., implemented using components of the example computer system 2300illustrated and described in more detail with reference to FIG. 23 . Insome embodiments, the steps performed by the ML system 2200 are storedin memory on the computer device 2228 for execution. In otherembodiments, the output 2224 is displayed on the apparatus 100 orelectronic displays of the cloud computing system 220.

A CNN is a type of feed-forward artificial neural network in which theconnectivity pattern between its neurons is inspired by the organizationof a visual cortex. Individual cortical neurons respond to stimuli in arestricted area of space known as the receptive field. The receptivefields of different neurons partially overlap such that they tile thevisual field. The response of an individual neuron to stimuli within itsreceptive field is approximated mathematically by a convolutionoperation. CNNs are based on biological processes and are variations ofmultilayer perceptrons designed to use minimal amounts of preprocessing.

In embodiments, the ML model 2216 is a CNN that includes bothconvolutional layers and max pooling layers. For example, thearchitecture of the ML model 2216 is “fully convolutional,” which meansthat variable sized sensor data vectors are fed into it. Forconvolutional layers, the ML model 2216 specifies a kernel size, astride of the convolution, and an amount of zero padding applied to theinput of that layer. For the pooling layers, the model 2216 specifiesthe kernel size and stride of the pooling.

In some embodiments, the ML system 2200 trains the ML model 2216, basedon the training data 2220, to correlate the feature vector 2212 toexpected outputs in the training data 2220. As part of the training ofthe ML model 2216, the ML system 2200 forms a training set of featuresand training labels by identifying a positive training set of featuresthat have been determined to have a desired property in question, and,in some embodiments, forms a negative training set of features that lackthe property in question.

The ML system 2200 applies ML techniques to train the ML model 2216,that when applied to the feature vector 2212, outputs indications ofwhether the feature vector 2212 has an associated desired property orproperties, such as a probability that the feature vector 2212 has aparticular Boolean property, or an estimated value of a scalar property.In embodiments, the ML system 2200 further applies dimensionalityreduction (e.g., via linear discriminant analysis (LDA), PCA, or thelike) to reduce the amount of data in the feature vector 2212 to asmaller, more representative set of data.

In embodiments, the ML system 2200 uses supervised ML to train the MLmodel 2216, with feature vectors of the positive training set and thenegative training set serving as the inputs. In some embodiments,different ML techniques, such as linear support vector machine (linearSVM), boosting for other algorithms (e.g., AdaBoost), logisticregression, naïve Bayes, memory-based learning, random forests, baggedtrees, decision trees, boosted trees, boosted stumps, neural networks,CNNs, etc., are used. In some example embodiments, a validation set 2232is formed of additional features, other than those in the training data2220, which have already been determined to have or to lack the propertyin question. The ML system 2200 applies the trained ML model 2216 to thefeatures of the validation set 2232 to quantify the accuracy of the MLmodel 2216. Common metrics applied in accuracy measurement includePrecision and Recall, where Precision refers to a number of results theML model 2216 correctly predicted out of the total it predicted, andRecall is a number of results the ML model 2216 correctly predicted outof the total number of features that had the desired property inquestion. In some embodiments, the ML system 2200 iteratively re-trainsthe ML model 2216 until the occurrence of a stopping condition, such asthe accuracy measurement indication that the ML model 2216 issufficiently accurate, or a number of training rounds having takenplace. In embodiments, the validation set 2232 includes datacorresponding to confirmed locations, dates, times, activities, orcombinations thereof. This allows the detected values to be validatedusing the validation set 2232. The validation set 2232 is generatedbased on the analysis to be performed.

FIG. 23 is a block diagram illustrating an example computer system, inaccordance with one or more embodiments. Components of the examplecomputer system 2300 are used to implement the smart radios 224, thecloud computing system 220, and the smart camera 236 illustrated anddescribed in more detail with reference to FIG. 2A. In some embodiments,components of the example computer system 2300 are used to implement theML system 2200 illustrated and described in more detail with referenceto FIG. 22 . At least some operations described herein are implementedon the computer system 2300.

The computer system 2300 includes one or more central processing units(“processors”) 2302, main memory 2306, non-volatile memory 2310, networkadapters 2312 (e.g., network interface), video displays 2318,input/output devices 2320, control devices 2322 (e.g., keyboard andpointing devices), drive units 2324 including a storage medium 2326, anda signal generation device 2320 that are communicatively connected to abus 2316. The bus 2316 is illustrated as an abstraction that representsone or more physical buses and/or point-to-point connections that areconnected by appropriate bridges, adapters, or controllers. Inembodiments, the bus 2316, includes a system bus, a Peripheral ComponentInterconnect (PCI) bus or PCI-Express bus, a HyperTransport or industrystandard architecture (ISA) bus, a small computer system interface(SCSI) bus, a universal serial bus (USB), IIC (I2C) bus, or an Instituteof Electrical and Electronics Engineers (IEEE) standard 1394 bus (alsoreferred to as “Firewire”).

In embodiments, the computer system 2300 shares a similar computerprocessor architecture as that of a desktop computer, tablet computer,personal digital assistant (PDA), mobile phone, game console, musicplayer, wearable electronic device (e.g., a watch or fitness tracker),network-connected (“smart”) device (e.g., a television or home assistantdevice), virtual/augmented reality systems (e.g., a head-mounteddisplay), or another electronic device capable of executing a set ofinstructions (sequential or otherwise) that specify action(s) to betaken by the computer system 2300.

While the main memory 2306, non-volatile memory 2310, and storage medium2326 (also called a “machine-readable medium”) are shown to be a singlemedium, the term “machine-readable medium” and “storage medium” shouldbe taken to include a single medium or multiple media (e.g., acentralized/distributed database and/or associated caches and servers)that store one or more sets of instructions 2328. The term“machine-readable medium” and “storage medium” shall also be taken toinclude any medium that is capable of storing, encoding, or carrying aset of instructions for execution by the computer system 2300.

In general, the routines executed to implement the embodiments of thedisclosure are implemented as part of an operating system or a specificapplication, component, program, object, module, or sequence ofinstructions (collectively referred to as “computer programs”). Thecomputer programs typically include one or more instructions (e.g.,instructions 2304, 2308, 2328) set at various times in various memoryand storage devices in a computer device. When read and executed by theone or more processors 2302, the instruction(s) cause the computersystem 2300 to perform operations to execute elements involving thevarious aspects of the disclosure.

Moreover, while embodiments have been described in the context of fullyfunctioning computer devices, those skilled in the art will appreciatethat the various embodiments are capable of being distributed as aprogram product in a variety of forms. The disclosure applies regardlessof the particular type of machine or computer-readable media used toactually effect the distribution.

Further examples of machine-readable storage media, machine-readablemedia, or computer-readable media include recordable-type media such asvolatile and non-volatile memory devices 2310, floppy and otherremovable disks, hard disk drives, optical discs (e.g., Compact DiscRead-Only Memory (CD-ROMS), Digital Versatile Discs (DVDs)), andtransmission-type media such as digital and analog communication links.

The network adapter 2312 enables the computer system 2300 to mediatedata in a network 2314 with an entity that is external to the computersystem 2300 through any communication protocol supported by the computersystem 2300 and the external entity. In embodiments, the network adapter2312 includes a network adapter card, a wireless network interface card,a router, an access point, a wireless router, a switch, a multilayerswitch, a protocol converter, a gateway, a bridge, a bridge router, ahub, a digital media receiver, and/or a repeater.

In embodiments, the network adapter 2312 includes a firewall thatgoverns and/or manages permission to access proxy data in a computernetwork and tracks varying levels of trust between different machinesand/or applications. In embodiments, the firewall is any number ofmodules having any combination of hardware and/or software componentsable to enforce a predetermined set of access rights between aparticular set of machines and applications, machines and machines,and/or applications and applications (e.g., to regulate the flow oftraffic and resource sharing between these entities). The firewalladditionally manages and/or has access to an access control list thatdetails permissions including the access and operation rights of anobject by an individual, a machine, and/or an application, and thecircumstances under which the permission rights stand.

In embodiments, the functions performed in the processes and methods areimplemented in differing order. Furthermore, the outlined steps andoperations are only provided as examples. For example, some of the stepsand operations are optional, combined into fewer steps and operations,or expanded into additional steps and operations without detracting fromthe essence of the disclosed embodiments.

In embodiments, the techniques introduced here are implemented byprogrammable circuitry (e.g., one or more microprocessors), softwareand/or firmware, special-purpose hardwired (i.e., non-programmable)circuitry, or a combination of such forms. In embodiments,special-purpose circuitry is in the form of one or moreapplication-specific integrated circuits (ASICs), programmable logicdevices (PLDs), field-programmable gate arrays (FPGAs), etc.

The description and drawings herein are illustrative and are not to beconstrued as limiting. Numerous specific details are described toprovide a thorough understanding of the disclosure. However, in certaininstances, well-known details are not described in order to avoidobscuring the description. Further, various modifications can be madewithout deviating from the scope of the embodiments.

The terms used in this specification generally have their ordinarymeanings in the art, within the context of the disclosure, and in thespecific context where each term is used. Certain terms that are used todescribe the disclosure are discussed above, or elsewhere in thespecification, to provide additional guidance to the practitionerregarding the description of the disclosure. It will be appreciated thatthe same thing can be said in more than one way. One will recognize that“memory” is one form of a “storage” and that the terms are on occasionused interchangeably.

Consequently, alternative language and synonyms are used for any one ormore of the terms discussed herein, nor is any special significance tobe placed upon whether or not a term is elaborated or discussed herein.Synonyms for certain terms are provided. A recital of one or moresynonyms does not exclude the use of other synonyms. The use of examplesanywhere in this specification, including examples of any term discussedherein, is illustrative only and is not intended to further limit thescope and meaning of the disclosure or of any exemplified term.Likewise, the disclosure is not limited to various embodiments given inthis specification.

1. A computer-implemented method for enhancing accident reporting viasmart walkie-talkie tracking in a facility, the computer-implementedmethod comprising: obtaining, by a cloud computing system, trackedlocations of a smart walkie-talkie during a time period in which thesmart walkie-talkie is associated with a worker of the facility, whereinthe time period is defined based on time-logging information for thesmart walkie-talkie; detecting, by the cloud computing system, aplurality of (i) worker-equipment interactions between the worker andequipment of the facility and (ii) worker-worker collaborations betweenthe worker and other workers of the facility based on the trackedlocations of the smart walkie-talkie during the time period, wherein agiven worker-equipment interaction is detected according to acorrelation between a tracked location of the smart walkie-talkie and anestimated location of a given equipment, and wherein a givenworker-worker collaboration is detected according to a correlationbetween a tracked location of the smart walkie-talkie and a location ofa second smart walkie-talkie associated with another worker; and inresponse to a detection of an accident event based on sensor datacollected by the cloud computing system from sensor devices locatedthroughout the facility, automatically building, by the cloud computingsystem, an evidentiary data log for the accident event, wherein theevidentiary data log identifies, for the worker, at least one firstworker-equipment interaction or first worker-worker collaboration thatoccurred temporally adjacent to the accident event.
 2. Thecomputer-implemented method of claim 1, wherein the evidentiary data logincludes, for the worker, a timeline of the tracked locations of thesmart walkie-talkie before and after the accident event.
 3. Thecomputer-implemented method of claim 2, further comprising: providing,by the cloud computing system, a dynamic visualization of the timelineof the tracked locations included in the evidentiary data log, whereinthe dynamic visualization includes a slider interface that enables auser to select a time slice within the timeline, whereupon the dynamicvisualization displays a particular tracked location of the smartwalkie-talkie at the time slice.
 4. The computer-implemented method ofclaim 1, further comprising: associating portions of the evidentiarydata log with different persistence levels that control whether theportions of the evidentiary data log remain associated with the workersubsequent to the worker no longer being associated with the facility ora current employer, wherein the at least one first worker-equipmentinteraction or first worker-worker collaboration is associated with afirst persistence level that is disassociated from the worker subsequentto the worker no longer being associated with the current employer. 5.The computer-implemented method of claim 4, further comprising: enablingaccess to portions of the evidentiary data log associated with the firstpersistence level by users associated with the facility and the currentemployer subsequent to the worker no longer being associated with thefacility and the current employer; and restricting access to theportions of the evidentiary data log associated with the firstpersistence level by the worker subsequent to the worker no longer beingassociated with the facility and the current employer.
 6. Thecomputer-implemented method of claim 4, wherein the evidentiary data logincludes a risk metric associated with the worker, wherein the riskmetric is associated with a second persistence level that remainsassociated with the worker subsequent to the worker no longer beingassociated with the facility and the current employer.
 7. Thecomputer-implemented method of claim 1, further comprising: incrementinga risk metric for the worker based on the at least one firstworker-equipment interaction or first worker-worker collaboration beingin a predefined geofence in which the accident event occurred.
 8. Thecomputer-implemented method of claim 1, further comprising: incrementinga risk metric for the worker based on the accident event being linked toa particular equipment of the first worker-equipment interaction.
 9. Thecomputer-implemented method of claim 1, further comprising: generating,by the cloud computing system, a format for a worker experience datarecord of the worker using a machine learning (ML) model that is trainedto identify a predefined format template based on an extracted featurevector that describes the plurality of worker-equipment interactions andworker-worker collaborations detected for the worker; generating, by thecloud computing system, the worker experience data record with eventinformation for a subset of worker-equipment interactions andworker-worker collaborations according to the format; and publishing, bythe cloud computing system, the worker experience data record of theworker to a user profile associated with the worker on a profiling dataplatform.
 10. The computer-implemented method of claim 9, wherein the MLmodel is trained to include, in the format, a particular data fieldrelated to an experience level with a particular equipment, based on atleast a threshold amount of worker-equipment interactions between theworker and the particular equipment being detected.
 11. Thecomputer-implemented method of claim 9, wherein the ML model is trainedon training data that includes a plurality of stored worker experiencedata records having different sets of data fields.
 12. Thecomputer-implemented method of claim 1, further comprising: obtainingthe estimated location of the given equipment of the facility based on:obtaining, by the cloud computing system, tag detection data from eachof at least three smart walkie-talkies, wherein the tag detection dataidentifies a tag device that is physically attached to the givenequipment; and triangulating, by the cloud computing system, theestimated location of the given equipment based on (i) the tag detectiondata from each of the at least three smart walkie-talkies, and (ii) atracked location of each of the at least three smart walkie-talkies. 13.The computer-implemented method of claim 1, further comprising:obtaining, by the cloud computing system, the estimated location of thegiven equipment of the facility based on analyzing video data thatcaptures the given equipment.
 14. A computing system comprising: atleast one processor; and at least one memory storing instructions that,when executed by the at least one processor, cause the computing systemto: obtain tracked locations of a two-way radio transceiver deviceduring a time period in which the two-way radio transceiver device isassociated with a worker of a facility; detect a plurality of (i)worker-equipment interactions between the worker and equipment of thefacility and/or (ii) worker-worker collaborations between the worker andother workers of the facility based on the tracked locations of thetwo-way radio transceiver device; and in response to a detection of anaccident event based on sensor data collected from sensor deviceslocated throughout the facility, automatically build an evidentiary datalog for the accident event that includes, for the worker, a firstworker-equipment interaction or a first worker-worker collaboration thatoccurred temporally adjacent to the accident event.
 15. The computingsystem of claim 14, wherein the instructions further cause the computingsystem to: determine, based on the tracked locations, that the workerwas located in a predefined geofence where the accident event occurred,wherein the evidentiary data log is built based on determining that theworker was located in the predefined geofence.
 16. The computing systemof claim 14, wherein the evidentiary data log includes a path traveledby the worker before and after the accident event according to thetracked locations.
 17. The computing system of claim 14, wherein theinstructions further cause the computing system to: associate portionsof the evidentiary data log with respective persistence levels; andsubsequent to the worker no longer being associated with a currentemployer at the facility, selectively including first portionsassociated with a first persistence level with worker informationprovided to a subsequent employer over second portions associated with asecond persistence level.
 18. The computing system of claim 14, whereinthe instructions further cause the computing system to: include a riskmetric of the worker in the evidentiary data log, wherein the riskmetric is determined based on a proximity of a location of the firstworker-equipment interaction or the first worker-worker collaboration toa location of the accident event.
 19. The computing system of claim 14,wherein the instructions further cause the computing system to: use anML model to identify significant worker-equipment interactions from theplurality of worker-equipment interactions, wherein the ML model istrained to identify the significant worker-equipment interactions basedon historical worker experience data records; and update a workerexperience data record associated with the worker based on thesignificant worker-equipment interactions.
 20. A non-transitorycomputer-readable medium having executable instructions stored thereon,the executable instructions when executed by at least one processorcause the at least one processor to: obtain tracked locations of atwo-way radio transceiver device during a time period in which thetwo-way radio transceiver device is associated with a particular workerof a facility; detect a plurality of facility events experienced by theparticular worker based on correlating the tracked locations of thetwo-way radio transceiver device with locations of other equipment orother two-way radio transceiver devices during the time period; and inresponse to a detection of an accident event based on sensor datacollected from sensor devices located throughout the facility,automatically generate an evidentiary data log for the accident eventthat includes at least one first facility event that occurred temporallyadjacent to the accident event.